Contexte

Chaque enregistrement correpond à un patient qui a réalisé une ponction lombère (LCR). Une LCR n’étant pas réalisée pour des raisons mineurs, tous les patients de la BDD ont présentés des problèmes cognitifs.

Objectif

L’objectif de l’étude est d’analyser l’association entre différents biomarqueurs sanguins et les protéines Abeta et tau, marqueurs de la maladie d’Alzheimer.

Analyses Univariées

Dimension de la BDD

## [1] "Nombre d'observations : 1723"
## [1] "Nombre de variables : 176"

Variables socio-démographique

Age

Summary

Boxplot

Histogramme

## $breaks
##  [1] 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95
## 
## $counts
##  [1]   1   0   1   2   3  12  38 104 163 226 369 335 272 155  40   2
## 
## $density
##  [1] 0.0001160766 0.0000000000 0.0001160766 0.0002321532 0.0003482298
##  [6] 0.0013929193 0.0044109112 0.0120719675 0.0189204875 0.0262333140
## [11] 0.0428322693 0.0388856645 0.0315728381 0.0179918746 0.0046430644
## [16] 0.0002321532
## 
## $mids
##  [1] 17.5 22.5 27.5 32.5 37.5 42.5 47.5 52.5 57.5 62.5 67.5 72.5 77.5 82.5 87.5
## [16] 92.5
## 
## $xname
## [1] "d"
## 
## $equidist
## [1] TRUE
## 
## attr(,"class")
## [1] "histogram"

Sexe

Distribution

Niveau d’étude

Légende

1 = non scolarisé
2 = primaire sans diplôme
3 = certificat d’études
4 = secondaire (collège/CAP/brevet)
5 = secondaire (BEP/lycée)
6 = baccalauréat
7 = études supérieures
8 = non renseigné

Modalités normales

Recodage des Modalités

Barplot

Statut Marital

Distribution

Barplot

## [1] "Jamais marié"      "Divorcé"           "Marié / en couple"
## [4] "Veuf"

Nombre d’enfants

Modalités normales

Recodage des Modalités

Barplot

Mode de vie

Distribution

Barplot

Comportement de santé

Tabac

Distribution

Barplot

Alcool

Distribution

Barplot

Variables de santé

IMC

Summary

Boxplot

Histogramme

Découpage

Barplot

## [1] "Insuffisance pondérale"     "Corpulence normale"        
## [3] "Obésité modérée"            "Obésité morbide ou massive"
## [5] "Obésité sévère"             "Surpoids"

Fréquence Cardiaque

Summary

Boxplot

Histogramme

Distribution

Tension Artérielle Systolique

Summary

Boxplot

Histogramme

Tension Artérielle Diastolique

Summary

Boxplot

Histogramme

Hyper Tension Artérielle

Distribution

Barplot

Dyslipidemie

Distribution

Barplot

Les traitements

Table médicament restant

data2 = clean_names(data2)
L = c("trt1","trt2","trt3","trt4","trt5","trt6","trt7","trt8","trt9","trt10",
"trt11","trt12","trt13","trt14","trt15","trt16","trt17","trt18","trt19","trt20")

trt_total = data.frame(x = data2$trt1)

for (i in 2:length(L)){
  b = data.frame(x = data2[,L[i]])
  
  trt_total = rbind(trt_total,b)
}

trt_total = trt_total %>%
      mutate(x = tolower(x))%>% 
      filter(x != c(""))

### Algo

for (i in 1:nrow(trt_total)){
    trt_total[i,1] = gsub("y","i", trt_total[i,1])
    trt_total[i,1] = gsub("ll","l", trt_total[i,1])
  trt_total[i,1] = gsub("ff","f", trt_total[i,1])
  trt_total[i,1] = gsub("ph","f", trt_total[i,1])
  trt_total[i,1] = gsub("h","", trt_total[i,1])
  trt_total[i,1] = gsub("pp","p", trt_total[i,1])
  
  trt_total[i,1] = gsub(",","", trt_total[i,1])
  trt_total[i,1] = gsub("_","", trt_total[i,1])
  #trt_total[i,1] = gsub(".","", trt_total[i,1])
  
  trt_total[i,1] = gsub("0","", trt_total[i,1])
  trt_total[i,1] = gsub("1","", trt_total[i,1])
  trt_total[i,1] = gsub("2","", trt_total[i,1])
  trt_total[i,1] = gsub("3","", trt_total[i,1])
  trt_total[i,1] = gsub("4","", trt_total[i,1])
  trt_total[i,1] = gsub("5","", trt_total[i,1])
  trt_total[i,1] = gsub("6","", trt_total[i,1])
  trt_total[i,1] = gsub("7","", trt_total[i,1])
  trt_total[i,1] = gsub("8","", trt_total[i,1])
  trt_total[i,1] = gsub("9","", trt_total[i,1])

  if (length(grep(" ", trt_total[i,1], ignore.case = TRUE)) > 0){
    pos = regexpr(pattern=" ",trt_total[i,1],fixed=TRUE)[1]
    trt_total[i,1] = substr(trt_total[i,1],1,pos-1)
}
  trt_total[i,1] = gsub(" ","", trt_total[i,1])
  
  if(substr(trt_total[i,1],nchar(trt_total[i,1]),nchar(trt_total[i,1])) == "e"){
    trt_total[i,1] = gsub('.{1}$', '', trt_total[i,1])
  }

}

medi_total = c(medi_chol,medi_diabete,medi_HTA,medi_antidouleur,medi_anxiete,medi_cv,medi_depression,medi_ma,medi_parkinson,medi_sommeil,medi_trouble,medi_vitamines)

trt_total = trt_total %>%
      mutate(x = tolower(x))%>% 
      filter(x != c(""),!x %in% medi_total)


dat_test <- data.frame(x = trt_total)
med_nb = dat_test %>%
            mutate(x = tolower(x)) %>%
            filter(dat_test != "",!x %in% medi_total)%>%
            gather(value = "Modalités") %>%
            group_by(Modalités) %>%
            summarise(n = n()) %>% arrange(desc(n))    

med_nb = as.data.frame(med_nb)

med_nb
#write_xlsx(med_nb,"//172.27.137.244/g_boilay/alternance/export/med.xlsx")
#write.csv(med_nb,"//172.27.137.244/g_boilay/alternance/export/med.csv")

MMSE

Summary

Boxplot

Histogramme

Découpage

Barplot

APOE

Distribution

Distribution présence E4

Barplot

Diagnostique clinique

Légende

1 = MA
2 = MCI amnésique
3 = MCI autre
4= Démence mixte
5 = Démence vasculaire
6 = DFT
7 = Lewy
8 = Atrophie corticale postérieure
9 = Démence alcoolique
10 = Aphasie progressive primaire
11 = Trouble psychiatrique
12 = VIH
13 = NSP
14 = Autre

Distribution

Barplot

Biomarqueur de la MA

Abeta42

Distribution

Barplot

Standardisation

Boxplot

Histogramme

Tau

Distribution

Barplot

Standardisation

Boxplot

Histogramme

Biomarqueur sanguin

Cholesterol Total (g/l)

Summary

Boxplot

Histogramme

Découpage

Cholesterol hdl (g/l)

Summary

Boxplot

Histogramme

Découpage

Cholesterol ldl (g/l)

Summary

Boxplot

Histogramme

Glucose à jeun (mmol/l)

Summary

Boxplot

Histogramme

Découpage

Triglycerides (g/l)

Summary

Boxplot

Histogramme

Autres biomarqueurs

Folates (nmol/l)

Summary

Boxplot

Histogramme

Découpage

Vitamine B12 (pmol/l)

Summary

Boxplot

Histogramme

Histogramme

Découpage

Vitamine D (nmol/l)

Summary

Boxplot

Histogramme

Découpage

Proteines (g/l)

Summary

Boxplot

Histogramme

Découpage

TSH (mu/l)

Summary

Boxplot

Histogramme

Découpage

Uree (mmol/l)

Summary

Boxplot

Histogramme

Découpage

Creatinine (umol/l)

Summary

Boxplot

Histogramme

Découpage

Calcium (mmol/l)

Summary

Boxplot

Histogramme

Découpage

## integer(0)


Études d’association

Les variables que nous cherchons à expliquer sont l’abeta42 et le tau.

Mise en forme d’abeta42 en modalité

Mise en forme de tau en modalité

Matrice de corrélation

Abeta42 en catégorie

Nombre d’observations dans les modèles

## [1] "Modèle"
## [1] " + abeta42t2"
## [1] "N = 1723"
## [1] " + glucose_jeun"
## [1] "N = 1502"
## [1] " + triglycerides"
## [1] "N = 1282"
## [1] " + cholesterol_total"
## [1] "N = 1281"
## [1] " + age"
## [1] "N = 1281"
## [1] " + sexe"
## [1] "N = 1281"
## [1] " + apoe.reg2"
## [1] "N = 1177"
## [1] " + ch"
## [1] "N = 1177"
## [1] " + mmse"
## [1] "N = 1099"
## [1] " + dia"
## [1] "N = 1099"
## [1] " + imc"
## [1] "N = 935"

Tableau de fréquence

abeta42 faible (N=484) abeta42 normal (N=451) Total (N=935) p value
Age < 0.001
- Mean (SD) 71.145 (8.236) 67.945 (9.425) 69.601 (8.968)
- Range 47.000 - 89.000 42.000 - 89.000 42.000 - 89.000
Sexe 0.015
- Femme 280 (57.9%) 225 (49.9%) 505 (54.0%)
- Homme 204 (42.1%) 226 (50.1%) 430 (46.0%)
Glucose 0.793
- Mean (SD) 5.758 (1.608) 5.788 (1.870) 5.773 (1.738)
- Range 0.720 - 18.500 0.920 - 24.500 0.720 - 24.500
Triglycerides 0.467
- Mean (SD) 0.950 (0.343) 0.967 (0.363) 0.958 (0.353)
- Range 0.380 - 2.020 0.150 - 2.020 0.150 - 2.020
Cholesterol 0.006
- Mean (SD) 2.171 (0.514) 2.079 (0.512) 2.127 (0.514)
- Range 1.030 - 6.640 0.970 - 5.900 0.970 - 6.640
Cholesterol hdl 0.143
- Mean (SD) 0.601 (0.168) 0.585 (0.152) 0.593 (0.161)
- Range 0.210 - 1.590 0.280 - 1.380 0.210 - 1.590
Cholesterol ldl < 0.001
- Mean (SD) 1.374 (0.406) 1.279 (0.383) 1.328 (0.398)
- Range 0.360 - 2.970 0.380 - 2.630 0.360 - 2.970
Présence E4 < 0.001
- 0 192 (39.7%) 331 (73.4%) 523 (55.9%)
- 1 220 (45.5%) 113 (25.1%) 333 (35.6%)
- 2 72 (14.9%) 7 (1.6%) 79 (8.4%)
MMSE < 0.001
- Mean (SD) 21.045 (5.517) 23.900 (4.410) 22.422 (5.211)
- Range 4.000 - 30.000 6.000 - 30.000 4.000 - 30.000
Traitement cholesterol 0.310
- 0 406 (83.9%) 389 (86.3%) 795 (85.0%)
- 1 78 (16.1%) 62 (13.7%) 140 (15.0%)
Traitement Diabete 0.190
- 0 454 (93.8%) 413 (91.6%) 867 (92.7%)
- 1 30 (6.2%) 38 (8.4%) 68 (7.3%)
Niveau d’étude 0.248
- N-Miss 31 36 67
- études supérieurs 169 (37.3%) 156 (37.6%) 325 (37.4%)
- niveau études intermédiaires 152 (33.6%) 157 (37.8%) 309 (35.6%)
- niveau études mineurs 132 (29.1%) 102 (24.6%) 234 (27.0%)

Modèles sur le Glucose

Légende
## [1] "N = 1062"

Modèle 1 : Glucose + age
Modèle 2 : Glucose + age + sexe
Modèle 3 : Glucose + age + sexe + APOE
Modèle 4 : Glucose + age + sexe + APOE + Traitements
Modèle 5 : Glucose + age + sexe + APOE + Traitements + MMSE
Modèle 6 : Glucose + age + sexe + APOE + Traitements + MMSE + IMC

Modèle 1

Modèle
## 
## Call:
## glm(formula = abeta42t2 ~ glucose_jeun + age, family = binomial(logit), 
##     data = data_am_g)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.5436  -1.1814   0.9169   1.1308   1.5433  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -2.500378   0.517826  -4.829 1.37e-06 ***
## glucose_jeun -0.027111   0.034891  -0.777    0.437    
## age           0.039116   0.007082   5.523 3.33e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1471.4  on 1061  degrees of freedom
## Residual deviance: 1439.5  on 1059  degrees of freedom
## AIC: 1445.5
## 
## Number of Fisher Scoring iterations: 4
Odds ratio
## Attente de la réalisation du profilage...

Modèle 2

Modèle
## 
## Call:
## glm(formula = abeta42t2 ~ glucose_jeun + age + sexe, family = binomial(logit), 
##     data = data_am_g)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -1.608  -1.177   0.871   1.117   1.647  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -3.212214   0.588858  -5.455 4.90e-08 ***
## glucose_jeun -0.009055   0.035625  -0.254   0.7994    
## age           0.040430   0.007133   5.668 1.45e-08 ***
## sexe          0.337535   0.128259   2.632   0.0085 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1471.4  on 1061  degrees of freedom
## Residual deviance: 1432.5  on 1058  degrees of freedom
## AIC: 1440.5
## 
## Number of Fisher Scoring iterations: 4
Odds ratio
## Attente de la réalisation du profilage...

Modèle 3

Modèle
## 
## Call:
## glm(formula = abeta42t2 ~ glucose_jeun + age + sexe + apoe.reg2, 
##     family = binomial(logit), data = data_am_g)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.3765  -0.9938   0.4435   1.0069   1.9476  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -3.961609   0.633271  -6.256 3.96e-10 ***
## glucose_jeun -0.024368   0.037950  -0.642    0.521    
## age           0.043812   0.007623   5.747 9.08e-09 ***
## sexe          0.326927   0.136957   2.387    0.017 *  
## apoe.reg2     1.254773   0.115784  10.837  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1471.4  on 1061  degrees of freedom
## Residual deviance: 1292.8  on 1057  degrees of freedom
## AIC: 1302.8
## 
## Number of Fisher Scoring iterations: 4
Odds ratio
## Attente de la réalisation du profilage...

Modèle 4

Modèle
## 
## Call:
## glm(formula = abeta42t2 ~ glucose_jeun + age + sexe + apoe.reg2 + 
##     ch + dia, family = binomial(logit), data = data_am_g)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.3826  -0.9898   0.4455   1.0034   1.9484  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -3.97917    0.64172  -6.201 5.62e-10 ***
## glucose_jeun -0.02507    0.04201  -0.597   0.5507    
## age           0.04422    0.00773   5.720 1.07e-08 ***
## sexe          0.32720    0.13697   2.389   0.0169 *  
## apoe.reg2     1.25621    0.11611  10.819  < 2e-16 ***
## ch           -0.06154    0.19064  -0.323   0.7468    
## dia           0.02249    0.27725   0.081   0.9354    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1471.4  on 1061  degrees of freedom
## Residual deviance: 1292.7  on 1055  degrees of freedom
## AIC: 1306.7
## 
## Number of Fisher Scoring iterations: 4
Odds ratio
## Attente de la réalisation du profilage...

Modèle 5

Modèle
## 
## Call:
## glm(formula = abeta42t2 ~ glucose_jeun + age + sexe + apoe.reg2 + 
##     ch + dia + mmse, family = binomial(logit), data = data_am_g)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.3994  -0.9332   0.3529   0.9654   1.9361  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -1.193250   0.744224  -1.603   0.1089    
## glucose_jeun -0.036122   0.042686  -0.846   0.3974    
## age           0.043277   0.008012   5.402 6.60e-08 ***
## sexe          0.245278   0.141668   1.731   0.0834 .  
## apoe.reg2     1.209507   0.118358  10.219  < 2e-16 ***
## ch           -0.042302   0.195932  -0.216   0.8291    
## dia           0.042077   0.285132   0.148   0.8827    
## mmse         -0.111050   0.014546  -7.634 2.27e-14 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1471.4  on 1061  degrees of freedom
## Residual deviance: 1228.1  on 1054  degrees of freedom
## AIC: 1244.1
## 
## Number of Fisher Scoring iterations: 4
Odds ratio
## Attente de la réalisation du profilage...

Modèle 6

Modèle
## 
## Call:
## glm(formula = abeta42t2 ~ glucose_jeun + age + sexe + apoe.reg2 + 
##     ch + dia + mmse + imc, family = binomial(logit), data = data_am_g)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.3836  -0.9303   0.3485   0.9616   2.0464  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   0.033108   0.866632   0.038  0.96953    
## glucose_jeun -0.012802   0.043970  -0.291  0.77093    
## age           0.041457   0.008081   5.130 2.90e-07 ***
## sexe          0.223409   0.142388   1.569  0.11664    
## apoe.reg2     1.184760   0.119049   9.952  < 2e-16 ***
## ch            0.002593   0.197607   0.013  0.98953    
## dia           0.062715   0.287094   0.218  0.82708    
## mmse         -0.111574   0.014579  -7.653 1.96e-14 ***
## imc          -0.047291   0.017031  -2.777  0.00549 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1471.4  on 1061  degrees of freedom
## Residual deviance: 1220.2  on 1053  degrees of freedom
## AIC: 1238.2
## 
## Number of Fisher Scoring iterations: 4
Odds ratio
## Attente de la réalisation du profilage...

Tableau récapitulatif

Characteristic Modèle 1 Modèle 2 Modèle 3 Modèle 4 Modèle 5 Modèle 6
OR1 95% CI1 p-value OR1 95% CI1 p-value OR1 95% CI1 p-value OR1 95% CI1 p-value OR1 95% CI1 p-value OR1 95% CI1 p-value
glucose_jeun 0.97 0.91, 1.04 0.4 0.99 0.92, 1.06 0.8 0.98 0.91, 1.05 0.5 0.98 0.90, 1.06 0.6 0.96 0.89, 1.05 0.4 0.99 0.91, 1.08 0.8
age 1.04 1.03, 1.05 <0.001 1.04 1.03, 1.06 <0.001 1.04 1.03, 1.06 <0.001 1.05 1.03, 1.06 <0.001 1.04 1.03, 1.06 <0.001 1.04 1.03, 1.06 <0.001
sexe 1.40 1.09, 1.80 0.008 1.39 1.06, 1.82 0.017 1.39 1.06, 1.82 0.017 1.28 0.97, 1.69 0.083 1.25 0.95, 1.65 0.12
apoe.reg2 3.51 2.81, 4.42 <0.001 3.51 2.81, 4.43 <0.001 3.35 2.67, 4.24 <0.001 3.27 2.60, 4.15 <0.001
ch 0.94 0.65, 1.37 0.7 0.96 0.65, 1.41 0.8 1.00 0.68, 1.48 >0.9
dia 1.02 0.59, 1.76 >0.9 1.04 0.59, 1.82 0.9 1.06 0.60, 1.87 0.8
mmse 0.89 0.87, 0.92 <0.001 0.89 0.87, 0.92 <0.001
imc 0.95 0.92, 0.99 0.005
1 OR = Odds Ratio, CI = Confidence Interval

Modèles sur la Triglycéride

Légende
## [1] "N = 962"

Modèle 1 : Triglycéride + age
Modèle 2 : Triglycéride + age + sexe
Modèle 3 : Triglycéride + age + sexe + APOE
Modèle 4 : Triglycéride + age + sexe + APOE + Traitements
Modèle 5 : Triglycéride + age + sexe + APOE + Traitements + MMSE
Modèle 6 : Triglycéride + age + sexe + APOE + Traitements + MMSE + IMC

Modèle 1

Modèle
## 
## Call:
## glm(formula = abeta42t2 ~ triglycerides + age, family = binomial(logit), 
##     data = data_am_t)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.5592  -1.1922   0.9114   1.1210   1.5407  
## 
## Coefficients:
##                Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   -2.605758   0.556204  -4.685 2.80e-06 ***
## triglycerides -0.070813   0.184389  -0.384    0.701    
## age            0.039595   0.007484   5.291 1.22e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1332.0  on 961  degrees of freedom
## Residual deviance: 1302.6  on 959  degrees of freedom
## AIC: 1308.6
## 
## Number of Fisher Scoring iterations: 4
Odds ratio
## Attente de la réalisation du profilage...

Modèle 2

Modèle
## 
## Call:
## glm(formula = abeta42t2 ~ triglycerides + age + sexe, family = binomial(logit), 
##     data = data_am_t)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.6275  -1.1824   0.8834   1.1120   1.5633  
## 
## Coefficients:
##                Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   -3.293534   0.615915  -5.347 8.92e-08 ***
## triglycerides -0.037037   0.185644  -0.200  0.84187    
## age            0.041014   0.007544   5.437 5.43e-08 ***
## sexe           0.361829   0.132684   2.727  0.00639 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1332.0  on 961  degrees of freedom
## Residual deviance: 1295.1  on 958  degrees of freedom
## AIC: 1303.1
## 
## Number of Fisher Scoring iterations: 4
Odds ratio
## Attente de la réalisation du profilage...

Modèle 3

Modèle
## 
## Call:
## glm(formula = abeta42t2 ~ triglycerides + age + sexe + apoe.reg2, 
##     family = binomial(logit), data = data_am_t)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.4287  -0.9772   0.4346   0.9763   1.8920  
## 
## Coefficients:
##                Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   -4.226190   0.669437  -6.313 2.74e-10 ***
## triglycerides -0.036934   0.200268  -0.184   0.8537    
## age            0.045165   0.008122   5.561 2.68e-08 ***
## sexe           0.348478   0.142805   2.440   0.0147 *  
## apoe.reg2      1.330147   0.123662  10.756  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1332.0  on 961  degrees of freedom
## Residual deviance: 1155.6  on 957  degrees of freedom
## AIC: 1165.6
## 
## Number of Fisher Scoring iterations: 4
Odds ratio
## Attente de la réalisation du profilage...

Modèle 4

Modèle
## 
## Call:
## glm(formula = abeta42t2 ~ triglycerides + age + sexe + apoe.reg2 + 
##     ch + dia, family = binomial(logit), data = data_am_t)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.4330  -0.9757   0.4301   0.9785   1.8863  
## 
## Coefficients:
##                Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   -4.210505   0.671931  -6.266 3.70e-10 ***
## triglycerides -0.025122   0.201053  -0.125   0.9006    
## age            0.045092   0.008236   5.475 4.38e-08 ***
## sexe           0.339107   0.143498   2.363   0.0181 *  
## apoe.reg2      1.328357   0.123814  10.729  < 2e-16 ***
## ch             0.054458   0.207306   0.263   0.7928    
## dia           -0.189006   0.273658  -0.691   0.4898    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1332.0  on 961  degrees of freedom
## Residual deviance: 1155.1  on 955  degrees of freedom
## AIC: 1169.1
## 
## Number of Fisher Scoring iterations: 4
Odds ratio
## Attente de la réalisation du profilage...

Modèle 5

Modèle
## 
## Call:
## glm(formula = abeta42t2 ~ triglycerides + age + sexe + apoe.reg2 + 
##     ch + dia + mmse, family = binomial(logit), data = data_am_t)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.4098  -0.9194   0.3757   0.9464   1.9758  
## 
## Coefficients:
##                Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   -1.516928   0.780623  -1.943   0.0520 .  
## triglycerides -0.101392   0.205252  -0.494   0.6213    
## age            0.044231   0.008526   5.188 2.13e-07 ***
## sexe           0.276219   0.147843   1.868   0.0617 .  
## apoe.reg2      1.258855   0.125675  10.017  < 2e-16 ***
## ch             0.068487   0.212689   0.322   0.7474    
## dia           -0.199196   0.282210  -0.706   0.4803    
## mmse          -0.107754   0.015390  -7.001 2.53e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1332.0  on 961  degrees of freedom
## Residual deviance: 1101.1  on 954  degrees of freedom
## AIC: 1117.1
## 
## Number of Fisher Scoring iterations: 4
Odds ratio
## Attente de la réalisation du profilage...

Modèle 6

Modèle
## 
## Call:
## glm(formula = abeta42t2 ~ triglycerides + age + sexe + apoe.reg2 + 
##     ch + dia + mmse + imc, family = binomial(logit), data = data_am_t)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.4214  -0.9213   0.3779   0.9471   2.0710  
## 
## Coefficients:
##                Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   -0.400336   0.906199  -0.442   0.6587    
## triglycerides -0.023539   0.208083  -0.113   0.9099    
## age            0.043332   0.008592   5.043 4.58e-07 ***
## sexe           0.245164   0.148821   1.647   0.0995 .  
## apoe.reg2      1.241490   0.126370   9.824  < 2e-16 ***
## ch             0.107645   0.214328   0.502   0.6155    
## dia           -0.133611   0.284301  -0.470   0.6384    
## mmse          -0.107439   0.015392  -6.980 2.95e-12 ***
## imc           -0.043708   0.017980  -2.431   0.0151 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1332.0  on 961  degrees of freedom
## Residual deviance: 1095.1  on 953  degrees of freedom
## AIC: 1113.1
## 
## Number of Fisher Scoring iterations: 4
Odds ratio
## Attente de la réalisation du profilage...

Tableau récapitulatif

Characteristic Modèle 1 Modèle 2 Modèle 3 Modèle 4 Modèle 5 Modèle 6
OR1 95% CI1 p-value OR1 95% CI1 p-value OR1 95% CI1 p-value OR1 95% CI1 p-value OR1 95% CI1 p-value OR1 95% CI1 p-value
triglycerides 0.93 0.65, 1.34 0.7 0.96 0.67, 1.39 0.8 0.96 0.65, 1.43 0.9 0.98 0.66, 1.45 >0.9 0.90 0.60, 1.35 0.6 0.98 0.65, 1.47 >0.9
age 1.04 1.03, 1.06 <0.001 1.04 1.03, 1.06 <0.001 1.05 1.03, 1.06 <0.001 1.05 1.03, 1.06 <0.001 1.05 1.03, 1.06 <0.001 1.04 1.03, 1.06 <0.001
sexe 1.44 1.11, 1.86 0.006 1.42 1.07, 1.88 0.015 1.40 1.06, 1.86 0.018 1.32 0.99, 1.76 0.062 1.28 0.95, 1.71 0.10
apoe.reg2 3.78 2.98, 4.84 <0.001 3.77 2.97, 4.83 <0.001 3.52 2.76, 4.53 <0.001 3.46 2.71, 4.45 <0.001
ch 1.06 0.70, 1.59 0.8 1.07 0.71, 1.63 0.7 1.11 0.73, 1.70 0.6
dia 0.83 0.48, 1.41 0.5 0.82 0.47, 1.42 0.5 0.87 0.50, 1.53 0.6
mmse 0.90 0.87, 0.92 <0.001 0.90 0.87, 0.93 <0.001
imc 0.96 0.92, 0.99 0.015
1 OR = Odds Ratio, CI = Confidence Interval

Modèles sur le Cholestérol

Légende
## [1] "N = 1006"

Modèle 1 : Cholestérol + age
Modèle 2 : Cholestérol + age + sexe
Modèle 3 : Cholestérol + age + sexe + APOE
Modèle 4 : Cholestérol + age + sexe + APOE + Traitements
Modèle 5 : Cholestérol + age + sexe + APOE + Traitements + MMSE
Modèle 6 : Cholestérol + age + sexe + APOE + Traitements + MMSE + IMC

Modèle 1

Modèle
## 
## Call:
## glm(formula = abeta42t2 ~ cholesterol_total + age, family = binomial(logit), 
##     data = data_am_c)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.9446  -1.1586   0.8581   1.1197   1.5276  
## 
## Coefficients:
##                    Estimate Std. Error z value Pr(>|z|)    
## (Intercept)       -3.723353   0.626945  -5.939 2.87e-09 ***
## cholesterol_total  0.393784   0.131439   2.996  0.00274 ** 
## age                0.042260   0.007404   5.708 1.14e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1393.8  on 1005  degrees of freedom
## Residual deviance: 1354.4  on 1003  degrees of freedom
## AIC: 1360.4
## 
## Number of Fisher Scoring iterations: 4
Odds ratio
## Attente de la réalisation du profilage...

Modèle 2

Modèle
## 
## Call:
## glm(formula = abeta42t2 ~ cholesterol_total + age + sexe, family = binomial(logit), 
##     data = data_am_c)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -1.853  -1.164   0.839   1.123   1.543  
## 
## Coefficients:
##                    Estimate Std. Error z value Pr(>|z|)    
## (Intercept)       -3.996785   0.642066  -6.225 4.82e-10 ***
## cholesterol_total  0.302169   0.137823   2.192   0.0283 *  
## age                0.042807   0.007427   5.763 8.25e-09 ***
## sexe               0.282006   0.136797   2.061   0.0393 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1393.8  on 1005  degrees of freedom
## Residual deviance: 1350.1  on 1002  degrees of freedom
## AIC: 1358.1
## 
## Number of Fisher Scoring iterations: 4
Odds ratio
## Attente de la réalisation du profilage...

Modèle 3

Modèle
## 
## Call:
## glm(formula = abeta42t2 ~ cholesterol_total + age + sexe + apoe.reg2, 
##     family = binomial(logit), data = data_am_c)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.4789  -0.9833   0.4258   1.0000   1.8767  
## 
## Coefficients:
##                    Estimate Std. Error z value Pr(>|z|)    
## (Intercept)       -4.688035   0.688074  -6.813 9.54e-12 ***
## cholesterol_total  0.188765   0.142824   1.322   0.1863    
## age                0.046406   0.007952   5.835 5.36e-09 ***
## sexe               0.308945   0.146143   2.114   0.0345 *  
## apoe.reg2          1.279867   0.120011  10.665  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1393.8  on 1005  degrees of freedom
## Residual deviance: 1215.2  on 1001  degrees of freedom
## AIC: 1225.2
## 
## Number of Fisher Scoring iterations: 4
Odds ratio
## Attente de la réalisation du profilage...

Modèle 4

Modèle
## 
## Call:
## glm(formula = abeta42t2 ~ cholesterol_total + age + sexe + apoe.reg2 + 
##     ch + dia, family = binomial(logit), data = data_am_c)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.4799  -0.9833   0.4254   1.0008   1.8782  
## 
## Coefficients:
##                    Estimate Std. Error z value Pr(>|z|)    
## (Intercept)       -4.699349   0.691618  -6.795 1.09e-11 ***
## cholesterol_total  0.190582   0.144377   1.320   0.1868    
## age                0.046448   0.008042   5.775 7.68e-09 ***
## sexe               0.310646   0.146485   2.121   0.0339 *  
## apoe.reg2          1.280625   0.120094  10.664  < 2e-16 ***
## ch                -0.011952   0.199579  -0.060   0.9522    
## dia                0.043510   0.262723   0.166   0.8685    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1393.8  on 1005  degrees of freedom
## Residual deviance: 1215.2  on  999  degrees of freedom
## AIC: 1229.2
## 
## Number of Fisher Scoring iterations: 4
Odds ratio
## Attente de la réalisation du profilage...

Modèle 5

Modèle
## 
## Call:
## glm(formula = abeta42t2 ~ cholesterol_total + age + sexe + apoe.reg2 + 
##     ch + dia + mmse, family = binomial(logit), data = data_am_c)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.5070  -0.9507   0.3601   0.9486   2.0180  
## 
## Coefficients:
##                    Estimate Std. Error z value Pr(>|z|)    
## (Intercept)       -2.204502   0.779108  -2.830  0.00466 ** 
## cholesterol_total  0.249671   0.148804   1.678  0.09338 .  
## age                0.046152   0.008331   5.540 3.02e-08 ***
## sexe               0.224276   0.151445   1.481  0.13863    
## apoe.reg2          1.208232   0.122048   9.900  < 2e-16 ***
## ch                 0.018260   0.204781   0.089  0.92895    
## dia                0.034869   0.270726   0.129  0.89752    
## mmse              -0.107932   0.015054  -7.170 7.53e-13 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1393.8  on 1005  degrees of freedom
## Residual deviance: 1158.6  on  998  degrees of freedom
## AIC: 1174.6
## 
## Number of Fisher Scoring iterations: 4
Odds ratio
## Attente de la réalisation du profilage...

Modèle 6

Modèle
## 
## Call:
## glm(formula = abeta42t2 ~ cholesterol_total + age + sexe + apoe.reg2 + 
##     ch + dia + mmse + imc, family = binomial(logit), data = data_am_c)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.4778  -0.9344   0.3494   0.9521   2.0426  
## 
## Coefficients:
##                    Estimate Std. Error z value Pr(>|z|)    
## (Intercept)       -1.065190   0.930610  -1.145   0.2524    
## cholesterol_total  0.222673   0.149946   1.485   0.1375    
## age                0.044793   0.008394   5.336 9.48e-08 ***
## sexe               0.201153   0.152256   1.321   0.1864    
## apoe.reg2          1.191730   0.122564   9.723  < 2e-16 ***
## ch                 0.048016   0.205996   0.233   0.8157    
## dia                0.090926   0.273085   0.333   0.7392    
## mmse              -0.107735   0.015061  -7.153 8.48e-13 ***
## imc               -0.038209   0.017098  -2.235   0.0254 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1393.8  on 1005  degrees of freedom
## Residual deviance: 1153.6  on  997  degrees of freedom
## AIC: 1171.6
## 
## Number of Fisher Scoring iterations: 4
Odds ratio
## Attente de la réalisation du profilage...

Tableau récapitulatif

Characteristic Modèle 1 Modèle 2 Modèle 3 Modèle 4 Modèle 5 Modèle 6
OR1 95% CI1 p-value OR1 95% CI1 p-value OR1 95% CI1 p-value OR1 95% CI1 p-value OR1 95% CI1 p-value OR1 95% CI1 p-value
triglycerides 0.93 0.65, 1.34 0.7 0.96 0.67, 1.39 0.8 0.96 0.65, 1.43 0.9 0.98 0.66, 1.45 >0.9 0.90 0.60, 1.35 0.6 0.98 0.65, 1.47 >0.9
age 1.04 1.03, 1.06 <0.001 1.04 1.03, 1.06 <0.001 1.05 1.03, 1.06 <0.001 1.05 1.03, 1.06 <0.001 1.05 1.03, 1.06 <0.001 1.04 1.03, 1.06 <0.001
sexe 1.44 1.11, 1.86 0.006 1.42 1.07, 1.88 0.015 1.40 1.06, 1.86 0.018 1.32 0.99, 1.76 0.062 1.28 0.95, 1.71 0.10
apoe.reg2 3.78 2.98, 4.84 <0.001 3.77 2.97, 4.83 <0.001 3.52 2.76, 4.53 <0.001 3.46 2.71, 4.45 <0.001
ch 1.06 0.70, 1.59 0.8 1.07 0.71, 1.63 0.7 1.11 0.73, 1.70 0.6
dia 0.83 0.48, 1.41 0.5 0.82 0.47, 1.42 0.5 0.87 0.50, 1.53 0.6
mmse 0.90 0.87, 0.92 <0.001 0.90 0.87, 0.93 <0.001
imc 0.96 0.92, 0.99 0.015
1 OR = Odds Ratio, CI = Confidence Interval

Abeta42 en continu

Nombre d’Observations dans les modèles

## [1] "Modèle"
## [1] " + abeta42_"
## [1] "N = 1721"
## [1] " + glucose_jeun"
## [1] "N = 1501"
## [1] " + triglycerides"
## [1] "N = 1281"
## [1] " + cholesterol_total"
## [1] "N = 1280"
## [1] " + age"
## [1] "N = 1280"
## [1] " + sexe"
## [1] "N = 1280"
## [1] " + apoe.reg2"
## [1] "N = 1176"
## [1] " + ch"
## [1] "N = 1176"
## [1] " + mmse"
## [1] "N = 1098"
## [1] " + imc"
## [1] "N = 934"
## [1] " + dia"
## [1] "N = 934"

Matrice de corrélation

Modèles sur le Glucose

Légende
## [1] "N = 1061"

Modèle 1 : Glucose + age
Modèle 2 : Glucose + age + sexe
Modèle 3 : Glucose + age + sexe + APOE
Modèle 4 : Glucose + age + sexe + APOE + Traitements
Modèle 5 : Glucose + age + sexe + APOE + Traitements + MMSE
Modèle 6 : Glucose + age + sexe + APOE + Traitements + MMSE + IMC

Modèle 1

Modèle
## 
## Call:
## lm(formula = abeta42_ ~ glucose_jeun + age, data = data_ac_g)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.2522 -0.7928 -0.2886  0.8518  5.5710 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)   
## (Intercept)   0.321998   0.252683   1.274  0.20283   
## glucose_jeun  0.048287   0.017553   2.751  0.00605 **
## age          -0.008519   0.003435  -2.480  0.01329 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.012 on 1058 degrees of freedom
## Multiple R-squared:  0.01178,    Adjusted R-squared:  0.009916 
## F-statistic: 6.308 on 2 and 1058 DF,  p-value: 0.001891
Scatter plot

Régression linéaire
## Warning in abline(abeta42_glucose.mod, col = "red"): utilisation des deux
## premiers des 3 coefficients de régression

Modèle 2

Modèle
## 
## Call:
## lm(formula = abeta42_ ~ glucose_jeun + age + sexe, data = data_ac_g)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.2430 -0.7944 -0.2981  0.8569  5.5714 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)   
## (Intercept)   0.359078   0.284562   1.262  0.20728   
## glucose_jeun  0.047319   0.017889   2.645  0.00829 **
## age          -0.008575   0.003442  -2.491  0.01289 * 
## sexe         -0.018057   0.063628  -0.284  0.77663   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.013 on 1057 degrees of freedom
## Multiple R-squared:  0.01186,    Adjusted R-squared:  0.009055 
## F-statistic: 4.229 on 3 and 1057 DF,  p-value: 0.005545
confint(mod, 'sexe', level=0.95)
##           2.5 %    97.5 %
## sexe -0.1429078 0.1067938
summary(mod)$coefficients
##                  Estimate  Std. Error    t value    Pr(>|t|)
## (Intercept)   0.359077530 0.284562175  1.2618597 0.207277663
## glucose_jeun  0.047318930 0.017889112  2.6451246 0.008287209
## age          -0.008574568 0.003442012 -2.4911500 0.012885544
## sexe         -0.018056997 0.063627600 -0.2837919 0.776625443
Scatter plot

Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 4
## coefficients de régression

Modèle 3

Modèle
## 
## Call:
## lm(formula = abeta42_ ~ glucose_jeun + age + sexe + apoe.reg2, 
##     data = data_ac_g)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.2073 -0.7938 -0.3108  0.8399  5.5089 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)   
## (Intercept)   0.394297   0.284458   1.386  0.16600   
## glucose_jeun  0.048223   0.017860   2.700  0.00704 **
## age          -0.008456   0.003436  -2.461  0.01401 * 
## sexe         -0.013350   0.063542  -0.210  0.83363   
## apoe.reg2    -0.107640   0.048069  -2.239  0.02535 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.011 on 1056 degrees of freedom
## Multiple R-squared:  0.01653,    Adjusted R-squared:  0.0128 
## F-statistic: 4.437 on 4 and 1056 DF,  p-value: 0.001466
Scatter plot

Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 5
## coefficients de régression

Modèle 4

Modèle
## 
## Call:
## lm(formula = abeta42_ ~ glucose_jeun + age + sexe + apoe.reg2 + 
##     ch + dia, data = data_ac_g)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.3452 -0.7965 -0.2960  0.8373  5.4255 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)   
## (Intercept)   0.366298   0.288365   1.270  0.20427   
## glucose_jeun  0.055361   0.019554   2.831  0.00473 **
## age          -0.008514   0.003486  -2.442  0.01476 * 
## sexe         -0.013709   0.063578  -0.216  0.82933   
## apoe.reg2    -0.111397   0.048299  -2.306  0.02128 * 
## ch            0.014411   0.088269   0.163  0.87034   
## dia          -0.119165   0.130848  -0.911  0.36266   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.011 on 1054 degrees of freedom
## Multiple R-squared:  0.0173, Adjusted R-squared:  0.01171 
## F-statistic: 3.093 on 6 and 1054 DF,  p-value: 0.005237
Scatter plot

Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 7
## coefficients de régression

Modèle 5

Modèle
## 
## Call:
## lm(formula = abeta42_ ~ glucose_jeun + age + sexe + apoe.reg2 + 
##     ch + dia + mmse, data = data_ac_g)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.3449 -0.8016 -0.2889  0.8364  5.4073 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)   
## (Intercept)   0.170117   0.331867   0.513  0.60834   
## glucose_jeun  0.056086   0.019560   2.867  0.00422 **
## age          -0.008275   0.003492  -2.370  0.01797 * 
## sexe         -0.007248   0.063796  -0.114  0.90956   
## apoe.reg2    -0.103763   0.048711  -2.130  0.03339 * 
## ch            0.011375   0.088288   0.129  0.89751   
## dia          -0.120472   0.130827  -0.921  0.35734   
## mmse          0.007206   0.006037   1.194  0.23291   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.011 on 1053 degrees of freedom
## Multiple R-squared:  0.01863,    Adjusted R-squared:  0.01211 
## F-statistic: 2.856 on 7 and 1053 DF,  p-value: 0.005901
Scatter plot

Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 8
## coefficients de régression

Modèle 6

Modèle
## 
## Call:
## lm(formula = abeta42_ ~ glucose_jeun + age + sexe + apoe.reg2 + 
##     ch + dia + mmse + imc, data = data_ac_g)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.3739 -0.8026 -0.2823  0.8396  5.3762 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)   
## (Intercept)   0.268034   0.388588   0.690  0.49049   
## glucose_jeun  0.058026   0.019972   2.905  0.00374 **
## age          -0.008446   0.003511  -2.406  0.01631 * 
## sexe         -0.009209   0.063947  -0.144  0.88552   
## apoe.reg2    -0.106274   0.049003  -2.169  0.03033 * 
## ch            0.014225   0.088516   0.161  0.87236   
## dia          -0.118092   0.130966  -0.902  0.36742   
## mmse          0.007172   0.006040   1.187  0.23532   
## imc          -0.003708   0.007648  -0.485  0.62788   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.011 on 1052 degrees of freedom
## Multiple R-squared:  0.01885,    Adjusted R-squared:  0.01139 
## F-statistic: 2.526 on 8 and 1052 DF,  p-value: 0.01005
Scatter plot

Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 9
## coefficients de régression

Tableau récapitulatif

Model 1Model 2Model 3Model 4Model 5Model 6
(Intercept)0.01   0.02   0.02   0.02   0.02   0.02   
[-0.05, 0.07],(0.74)  [-0.07, 0.11],(0.66)  [-0.07, 0.11],(0.70)  [-0.07, 0.12],(0.61)  [-0.07, 0.12],(0.66)  [-0.07, 0.12],(0.65)  
glucose_jeun0.09 **0.08 **0.09 **0.10 **0.10 **0.10 **
[0.02, 0.15],(0.01)  [0.02, 0.15],(0.01)  [0.02, 0.15],(0.01)  [0.03, 0.17],(0.00)  [0.03, 0.17],(0.00)  [0.03, 0.17],(0.00)  
age-0.08 * -0.08 * -0.08 * -0.08 * -0.08 * -0.08 * 
[-0.14, -0.02],(0.01)  [-0.14, -0.02],(0.01)  [-0.14, -0.02],(0.01)  [-0.14, -0.02],(0.01)  [-0.14, -0.01],(0.02)  [-0.14, -0.01],(0.02)  
sexe      -0.02   -0.01   -0.01   -0.01   -0.01   
      [-0.14, 0.11],(0.78)  [-0.14, 0.11],(0.83)  [-0.14, 0.11],(0.83)  [-0.13, 0.12],(0.91)  [-0.13, 0.12],(0.89)  
apoe.reg2            -0.07 * -0.07 * -0.07 * -0.07 * 
            [-0.13, -0.01],(0.03)  [-0.13, -0.01],(0.02)  [-0.13, -0.01],(0.03)  [-0.13, -0.01],(0.03)  
ch                  0.01   0.01   0.01   
                  [-0.16, 0.19],(0.87)  [-0.16, 0.18],(0.90)  [-0.16, 0.19],(0.87)  
dia                  -0.12   -0.12   -0.12   
                  [-0.38, 0.14],(0.36)  [-0.38, 0.14],(0.36)  [-0.38, 0.14],(0.37)  
mmse                        0.04   0.04   
                        [-0.02, 0.10],(0.23)  [-0.02, 0.10],(0.24)  
imc                              -0.02   
                              [-0.08, 0.05],(0.63)  
N1061      1061      1061      1061      1061      1061      
R20.01   0.01   0.02   0.02   0.02   0.02   
All continuous predictors are mean-centered and scaled by 1 standard deviation. *** p < 0.001; ** p < 0.01; * p < 0.05.

Modèles sur la Triglycéride

Légende
## [1] "N = 961"

Modèle 1 : Triglycéride + age
Modèle 2 : Triglycéride + age + sexe
Modèle 3 : Triglycéride + age + sexe + APOE
Modèle 4 : Triglycéride + age + sexe + APOE + Traitements
Modèle 5 : Triglycéride + age + sexe + APOE + Traitements + MMSE
Modèle 6 : Triglycéride + age + sexe + APOE + Traitements + MMSE + IMC

Modèle 1

Modèle
## 
## Call:
## lm(formula = abeta42_ ~ triglycerides + age, data = data_ac_t)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.1979 -0.8162 -0.2727  0.8537  5.9907 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)  
## (Intercept)    0.690215   0.273350   2.525   0.0117 *
## triglycerides -0.144098   0.092347  -1.560   0.1190  
## age           -0.007442   0.003659  -2.034   0.0422 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.018 on 958 degrees of freedom
## Multiple R-squared:  0.006705,   Adjusted R-squared:  0.004631 
## F-statistic: 3.233 on 2 and 958 DF,  p-value: 0.03985
Scatter plot

Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 3
## coefficients de régression

Modèle 2

Modèle
## 
## Call:
## lm(formula = abeta42_ ~ triglycerides + age + sexe, data = data_ac_t)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.2404 -0.8152 -0.2611  0.8476  5.9443 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)   
## (Intercept)    0.835103   0.299127   2.792  0.00535 **
## triglycerides -0.151771   0.092551  -1.640  0.10136   
## age           -0.007674   0.003663  -2.095  0.03644 * 
## sexe          -0.078787   0.066129  -1.191  0.23379   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.018 on 957 degrees of freedom
## Multiple R-squared:  0.008176,   Adjusted R-squared:  0.005067 
## F-statistic:  2.63 on 3 and 957 DF,  p-value: 0.04898
Scatter plot

Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 4
## coefficients de régression

Modèle 3

Modèle
## 
## Call:
## lm(formula = abeta42_ ~ triglycerides + age + sexe + apoe.reg2, 
##     data = data_ac_t)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.2866 -0.7963 -0.2712  0.8428  5.8990 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)   
## (Intercept)    0.870179   0.299414   2.906  0.00374 **
## triglycerides -0.150982   0.092444  -1.633  0.10275   
## age           -0.007597   0.003659  -2.076  0.03816 * 
## sexe          -0.074218   0.066101  -1.123  0.26180   
## apoe.reg2     -0.091341   0.050781  -1.799  0.07238 . 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.017 on 956 degrees of freedom
## Multiple R-squared:  0.01152,    Adjusted R-squared:  0.007386 
## F-statistic: 2.786 on 4 and 956 DF,  p-value: 0.02557
Scatter plot

Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 5
## coefficients de régression

Modèle 4

Modèle
## 
## Call:
## lm(formula = abeta42_ ~ triglycerides + age + sexe + apoe.reg2 + 
##     ch + dia, data = data_ac_t)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.2788 -0.8147 -0.2696  0.8463  5.9044 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)   
## (Intercept)    0.879069   0.300994   2.921  0.00358 **
## triglycerides -0.151329   0.092869  -1.629  0.10354   
## age           -0.007820   0.003717  -2.104  0.03563 * 
## sexe          -0.073304   0.066433  -1.103  0.27012   
## apoe.reg2     -0.091221   0.050905  -1.792  0.07345 . 
## ch             0.030683   0.095480   0.321  0.74801   
## dia            0.014427   0.130298   0.111  0.91186   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.018 on 954 degrees of freedom
## Multiple R-squared:  0.01166,    Adjusted R-squared:  0.005441 
## F-statistic: 1.875 on 6 and 954 DF,  p-value: 0.08211
Scatter plot

Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 7
## coefficients de régression

Modèle 5

Modèle
## 
## Call:
## lm(formula = abeta42_ ~ triglycerides + age + sexe + apoe.reg2 + 
##     ch + dia + mmse, data = data_ac_t)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.2877 -0.8085 -0.2659  0.8394  5.9002 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)  
## (Intercept)    0.791314   0.347122   2.280   0.0228 *
## triglycerides -0.149548   0.092971  -1.609   0.1080  
## age           -0.007714   0.003724  -2.072   0.0386 *
## sexe          -0.070977   0.066616  -1.065   0.2869  
## apoe.reg2     -0.087190   0.051539  -1.692   0.0910 .
## ch             0.029565   0.095542   0.309   0.7571  
## dia            0.014487   0.130349   0.111   0.9115  
## mmse           0.003262   0.006420   0.508   0.6115  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.018 on 953 degrees of freedom
## Multiple R-squared:  0.01192,    Adjusted R-squared:  0.004667 
## F-statistic: 1.643 on 7 and 953 DF,  p-value: 0.1197
Scatter plot

Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 8
## coefficients de régression

Modèle 6

Modèle
## 
## Call:
## lm(formula = abeta42_ ~ triglycerides + age + sexe + apoe.reg2 + 
##     ch + dia + mmse + imc, data = data_ac_t)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.2904 -0.8051 -0.2551  0.8408  5.8910 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)  
## (Intercept)    0.909149   0.408958   2.223   0.0264 *
## triglycerides -0.141928   0.094049  -1.509   0.1316  
## age           -0.007864   0.003735  -2.105   0.0355 *
## sexe          -0.074296   0.066918  -1.110   0.2672  
## apoe.reg2     -0.089830   0.051784  -1.735   0.0831 .
## ch             0.032643   0.095744   0.341   0.7332  
## dia            0.022714   0.131267   0.173   0.8627  
## mmse           0.003270   0.006422   0.509   0.6108  
## imc           -0.004398   0.008062  -0.545   0.5855  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.018 on 952 degrees of freedom
## Multiple R-squared:  0.01223,    Adjusted R-squared:  0.003933 
## F-statistic: 1.474 on 8 and 952 DF,  p-value: 0.1625
Scatter plot

Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 9
## coefficients de régression

Tableau récapitulatif

Model 1Model 2Model 3Model 4Model 5Model 6
(Intercept)0.03  0.08  0.07  0.07  0.07  0.07  
[-0.03, 0.10],(0.31) [-0.02, 0.17],(0.12) [-0.02, 0.17],(0.13) [-0.03, 0.17],(0.19) [-0.04, 0.17],(0.20) [-0.03, 0.17],(0.20) 
triglycerides-0.05  -0.05  -0.05  -0.05  -0.05  -0.05  
[-0.12, 0.01],(0.12) [-0.12, 0.01],(0.10) [-0.12, 0.01],(0.10) [-0.12, 0.01],(0.10) [-0.12, 0.01],(0.11) [-0.12, 0.02],(0.13) 
age-0.07 *-0.07 *-0.07 *-0.07 *-0.07 *-0.07 *
[-0.13, -0.00],(0.04) [-0.13, -0.00],(0.04) [-0.13, -0.00],(0.04) [-0.14, -0.00],(0.04) [-0.13, -0.00],(0.04) [-0.14, -0.00],(0.04) 
sexe     -0.08  -0.07  -0.07  -0.07  -0.07  
     [-0.21, 0.05],(0.23) [-0.20, 0.06],(0.26) [-0.20, 0.06],(0.27) [-0.20, 0.06],(0.29) [-0.21, 0.06],(0.27) 
apoe.reg2          -0.06  -0.06  -0.06  -0.06  
          [-0.12, 0.01],(0.07) [-0.12, 0.01],(0.07) [-0.12, 0.01],(0.09) [-0.12, 0.01],(0.08) 
ch               0.03  0.03  0.03  
               [-0.16, 0.22],(0.75) [-0.16, 0.22],(0.76) [-0.16, 0.22],(0.73) 
dia               0.01  0.01  0.02  
               [-0.24, 0.27],(0.91) [-0.24, 0.27],(0.91) [-0.23, 0.28],(0.86) 
mmse                    0.02  0.02  
                    [-0.05, 0.08],(0.61) [-0.05, 0.08],(0.61) 
imc                         -0.02  
                         [-0.09, 0.05],(0.59) 
N961     961     961     961     961     961     
R20.01  0.01  0.01  0.01  0.01  0.01  
All continuous predictors are mean-centered and scaled by 1 standard deviation. *** p < 0.001; ** p < 0.01; * p < 0.05.

Modèles sur le Cholestérol

Légende
## [1] "N = 1005"

Modèle 1 : Cholestérol + age
Modèle 2 : Cholestérol + age + sexe
Modèle 3 : Cholestérol + age + sexe + APOE
Modèle 4 : Cholestérol + age + sexe + APOE + Traitements
Modèle 5 : Cholestérol + age + sexe + APOE + Traitements + MMSE
Modèle 6 : Cholestérol + age + sexe + APOE + Traitements + MMSE + IMC

Modèle 1

Modèle
## 
## Call:
## lm(formula = abeta42_ ~ cholesterol_total + age, data = data_ac_c)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.1742 -0.8281 -0.2635  0.8471  6.0522 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        0.649197   0.299908   2.165   0.0306 *
## cholesterol_total -0.059883   0.062887  -0.952   0.3412  
## age               -0.007015   0.003599  -1.949   0.0516 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.021 on 1002 degrees of freedom
## Multiple R-squared:  0.004292,   Adjusted R-squared:  0.002304 
## F-statistic: 2.159 on 2 and 1002 DF,  p-value: 0.1159
Scatter plot

Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 3
## coefficients de régression

Modèle 2

Modèle
## 
## Call:
## lm(formula = abeta42_ ~ cholesterol_total + age + sexe, data = data_ac_c)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.1985 -0.8230 -0.2632  0.8375  6.0252 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        0.698118   0.307034   2.274   0.0232 *
## cholesterol_total -0.043910   0.066433  -0.661   0.5088  
## age               -0.007085   0.003601  -1.968   0.0494 *
## sexe              -0.051062   0.068329  -0.747   0.4551  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.021 on 1001 degrees of freedom
## Multiple R-squared:  0.004847,   Adjusted R-squared:  0.001865 
## F-statistic: 1.625 on 3 and 1001 DF,  p-value: 0.1819
Scatter plot

Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 4
## coefficients de régression

Modèle 3

Modèle
## 
## Call:
## lm(formula = abeta42_ ~ cholesterol_total + age + sexe + apoe.reg2, 
##     data = data_ac_c)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.2517 -0.8074 -0.2616  0.8583  5.9719 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        0.714809   0.306646   2.331   0.0199 *
## cholesterol_total -0.031217   0.066612  -0.469   0.6394  
## age               -0.006955   0.003596  -1.934   0.0533 .
## sexe              -0.050057   0.068220  -0.734   0.4633  
## apoe.reg2         -0.103362   0.050222  -2.058   0.0398 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.02 on 1000 degrees of freedom
## Multiple R-squared:  0.009044,   Adjusted R-squared:  0.005081 
## F-statistic: 2.282 on 4 and 1000 DF,  p-value: 0.05878
Scatter plot

Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 5
## coefficients de régression

Modèle 4

Modèle
## 
## Call:
## lm(formula = abeta42_ ~ cholesterol_total + age + sexe + apoe.reg2 + 
##     ch + dia, data = data_ac_c)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.2472 -0.8070 -0.2575  0.8599  5.9748 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        0.716796   0.308275   2.325   0.0203 *
## cholesterol_total -0.029337   0.067329  -0.436   0.6631  
## age               -0.007085   0.003643  -1.945   0.0521 .
## sexe              -0.050225   0.068439  -0.734   0.4632  
## apoe.reg2         -0.103588   0.050353  -2.057   0.0399 *
## ch                 0.020709   0.092994   0.223   0.8238  
## dia                0.003516   0.126131   0.028   0.9778  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.021 on 998 degrees of freedom
## Multiple R-squared:  0.009097,   Adjusted R-squared:  0.00314 
## F-statistic: 1.527 on 6 and 998 DF,  p-value: 0.1659
Scatter plot

Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 7
## coefficients de régression

Modèle 5

Modèle
## 
## Call:
## lm(formula = abeta42_ ~ cholesterol_total + age + sexe + apoe.reg2 + 
##     ch + dia + mmse, data = data_ac_c)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.2618 -0.8055 -0.2617  0.8473  5.9674 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        0.584556   0.347038   1.684   0.0924 .
## cholesterol_total -0.032614   0.067455  -0.483   0.6289  
## age               -0.006939   0.003648  -1.902   0.0574 .
## sexe              -0.045238   0.068713  -0.658   0.5105  
## apoe.reg2         -0.096972   0.050987  -1.902   0.0575 .
## ch                 0.018139   0.093060   0.195   0.8455  
## dia                0.003948   0.126152   0.031   0.9750  
## mmse               0.005270   0.006348   0.830   0.4066  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.021 on 997 degrees of freedom
## Multiple R-squared:  0.009782,   Adjusted R-squared:  0.00283 
## F-statistic: 1.407 on 7 and 997 DF,  p-value: 0.1987
Scatter plot

Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 8
## coefficients de régression

Modèle 6

Modèle
## 
## Call:
## lm(formula = abeta42_ ~ cholesterol_total + age + sexe + apoe.reg2 + 
##     ch + dia + mmse + imc, data = data_ac_c)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.2681 -0.8062 -0.2525  0.8474  5.9504 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        0.763616   0.420997   1.814   0.0700 .
## cholesterol_total -0.036553   0.067673  -0.540   0.5892  
## age               -0.007211   0.003666  -1.967   0.0495 *
## sexe              -0.049148   0.068924  -0.713   0.4760  
## apoe.reg2         -0.100368   0.051198  -1.960   0.0502 .
## ch                 0.021676   0.093199   0.233   0.8161  
## dia                0.014765   0.126997   0.116   0.9075  
## mmse               0.005287   0.006350   0.833   0.4052  
## imc               -0.005825   0.007749  -0.752   0.4524  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.021 on 996 degrees of freedom
## Multiple R-squared:  0.01034,    Adjusted R-squared:  0.002394 
## F-statistic: 1.301 on 8 and 996 DF,  p-value: 0.2389
Scatter plot

Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 9
## coefficients de régression

Tableau récapitulatif

Model 1Model 2Model 3Model 4Model 5Model 6
(Intercept)0.03 0.06  0.06  0.06  0.05 0.05  
[-0.03, 0.10],(0.30)[-0.03, 0.16],(0.21) [-0.04, 0.15],(0.22) [-0.04, 0.16],(0.27) [-0.05, 0.15],(0.29)[-0.05, 0.16],(0.28) 
cholesterol_total-0.03 -0.02  -0.02  -0.02  -0.02 -0.02  
[-0.09, 0.03],(0.34)[-0.09, 0.04],(0.51) [-0.08, 0.05],(0.64) [-0.08, 0.05],(0.66) [-0.09, 0.05],(0.63)[-0.09, 0.05],(0.59) 
age-0.06 -0.06 *-0.06  -0.06  -0.06 -0.07 *
[-0.13, 0.00],(0.05)[-0.13, -0.00],(0.05) [-0.13, 0.00],(0.05) [-0.13, 0.00],(0.05) [-0.13, 0.00],(0.06)[-0.13, -0.00],(0.05) 
sexe    -0.05  -0.05  -0.05  -0.05 -0.05  
    [-0.19, 0.08],(0.46) [-0.18, 0.08],(0.46) [-0.18, 0.08],(0.46) [-0.18, 0.09],(0.51)[-0.18, 0.09],(0.48) 
apoe.reg2         -0.07 *-0.07 *-0.06 -0.06  
         [-0.13, -0.00],(0.04) [-0.13, -0.00],(0.04) [-0.13, 0.00],(0.06)[-0.13, 0.00],(0.05) 
ch              0.02  0.02 0.02  
              [-0.16, 0.20],(0.82) [-0.16, 0.20],(0.85)[-0.16, 0.20],(0.82) 
dia              0.00  0.00 0.01  
              [-0.24, 0.25],(0.98) [-0.24, 0.25],(0.98)[-0.23, 0.26],(0.91) 
mmse                   0.03 0.03  
                   [-0.04, 0.09],(0.41)[-0.04, 0.09],(0.41) 
imc                       -0.02  
                       [-0.09, 0.04],(0.45) 
N1005    1005     1005     1005     1005    1005     
R20.00 0.00  0.01  0.01  0.01 0.01  
All continuous predictors are mean-centered and scaled by 1 standard deviation. *** p < 0.001; ** p < 0.01; * p < 0.05.

Tau en catégorie

Nombre d’Observations dans les modèles

## [1] "Modèle"
## [1] " + taut2"
## [1] "N = 1723"
## [1] " + glucose_jeun"
## [1] "N = 1502"
## [1] " + triglycerides"
## [1] "N = 1282"
## [1] " + cholesterol_total"
## [1] "N = 1281"
## [1] " + age"
## [1] "N = 1281"
## [1] " + sexe"
## [1] "N = 1281"
## [1] " + apoe.reg2"
## [1] "N = 1177"
## [1] " + ch"
## [1] "N = 1177"
## [1] " + mmse"
## [1] "N = 1099"
## [1] " + dia"
## [1] "N = 1099"
## [1] " + imc"
## [1] "N = 935"

Tableau de fréquence

FALSE (N=413) TRUE (N=522) Total (N=935) p value
Age < 0.001
- Mean (SD) 67.172 (9.292) 71.523 (8.218) 69.601 (8.968)
- Range 42.000 - 88.000 49.000 - 89.000 42.000 - 89.000
Sexe < 0.001
- Femme 196 (47.5%) 309 (59.2%) 505 (54.0%)
- Homme 217 (52.5%) 213 (40.8%) 430 (46.0%)
Glucose 0.761
- Mean (SD) 5.753 (1.721) 5.788 (1.754) 5.773 (1.738)
- Range 0.720 - 24.500 0.920 - 20.900 0.720 - 24.500
Triglycerides 0.017
- Mean (SD) 0.989 (0.364) 0.934 (0.343) 0.958 (0.353)
- Range 0.150 - 2.020 0.340 - 2.020 0.150 - 2.020
Cholesterol 0.066
- Mean (SD) 2.092 (0.516) 2.154 (0.512) 2.127 (0.514)
- Range 0.970 - 5.900 1.030 - 6.640 0.970 - 6.640
Cholesterol hdl < 0.001
- Mean (SD) 0.572 (0.154) 0.610 (0.165) 0.593 (0.161)
- Range 0.220 - 1.590 0.210 - 1.100 0.210 - 1.590
Cholesterol ldl 0.055
- Mean (SD) 1.300 (0.383) 1.350 (0.408) 1.328 (0.398)
- Range 0.480 - 2.970 0.360 - 2.730 0.360 - 2.970
Présence E4 < 0.001
- 0 291 (70.5%) 232 (44.4%) 523 (55.9%)
- 1 103 (24.9%) 230 (44.1%) 333 (35.6%)
- 2 19 (4.6%) 60 (11.5%) 79 (8.4%)
MMSE < 0.001
- Mean (SD) 23.913 (4.453) 21.243 (5.462) 22.422 (5.211)
- Range 8.000 - 30.000 4.000 - 30.000 4.000 - 30.000
Traitement cholesterol 0.734
- 0 353 (85.5%) 442 (84.7%) 795 (85.0%)
- 1 60 (14.5%) 80 (15.3%) 140 (15.0%)
Traitement Diabete 0.619
- 0 381 (92.3%) 486 (93.1%) 867 (92.7%)
- 1 32 (7.7%) 36 (6.9%) 68 (7.3%)
Niveau d’étude 0.466
- N-Miss 29 38 67
- études supérieurs 141 (36.7%) 184 (38.0%) 325 (37.4%)
- niveau études intermédiaires 145 (37.8%) 164 (33.9%) 309 (35.6%)
- niveau études mineurs 98 (25.5%) 136 (28.1%) 234 (27.0%)

Modèles sur le Glucose

Légende
## [1] "N = 1062"

Modèle 1 : Glucose + age
Modèle 2 : Glucose + age + sexe
Modèle 3 : Glucose + age + sexe + APOE
Modèle 4 : Glucose + age + sexe + APOE + Traitements
Modèle 5 : Glucose + age + sexe + APOE + Traitements + MMSE
Modèle 6 : Glucose + age + sexe + APOE + Traitements + MMSE + IMC

Modèle 1

Modèle
## 
## Call:
## glm(formula = taut2 ~ glucose_jeun + age, family = binomial(logit), 
##     data = data_tm_g)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.7760  -1.1958   0.8034   1.0600   1.6739  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -3.675284   0.538412  -6.826 8.72e-12 ***
## glucose_jeun -0.036869   0.035453  -1.040    0.298    
## age           0.059360   0.007439   7.980 1.47e-15 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1459.5  on 1061  degrees of freedom
## Residual deviance: 1390.0  on 1059  degrees of freedom
## AIC: 1396
## 
## Number of Fisher Scoring iterations: 4
Odds ratio
## Attente de la réalisation du profilage...
##                     OR     2.5 % 97.5 %         p    
## (Intercept)  0.0253422 0.0087008 0.0719 8.722e-12 ***
## glucose_jeun 0.9638019 0.8989036 1.0341    0.2984    
## age          1.0611570 1.0459901 1.0770 1.468e-15 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Modèle 2

Modèle
## 
## Call:
## glm(formula = taut2 ~ glucose_jeun + age + sexe, family = binomial(logit), 
##     data = data_tm_g)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.9032  -1.1755   0.7584   1.0404   1.8355  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -4.802182   0.621516  -7.727 1.10e-14 ***
## glucose_jeun -0.009463   0.036407  -0.260    0.795    
## age           0.061940   0.007554   8.200 2.40e-16 ***
## sexe          0.518029   0.132239   3.917 8.95e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1459.5  on 1061  degrees of freedom
## Residual deviance: 1374.5  on 1058  degrees of freedom
## AIC: 1382.5
## 
## Number of Fisher Scoring iterations: 4
Odds ratio
## Attente de la réalisation du profilage...
##                     OR     2.5 % 97.5 %         p    
## (Intercept)  0.0082118 0.0023842 0.0273 1.105e-14 ***
## glucose_jeun 0.9905821 0.9225289 1.0652    0.7949    
## age          1.0638989 1.0484712 1.0800 2.400e-16 ***
## sexe         1.6787148 1.2965126 2.1777 8.952e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Modèle 3

Modèle
## 
## Call:
## glm(formula = taut2 ~ glucose_jeun + age + sexe + apoe.reg2, 
##     family = binomial(logit), data = data_tm_g)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.0514  -1.0820   0.6300   0.9997   2.0329  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -5.298000   0.645185  -8.212  < 2e-16 ***
## glucose_jeun -0.018725   0.037419  -0.500 0.616782    
## age           0.064048   0.007787   8.225  < 2e-16 ***
## sexe          0.508909   0.136188   3.737 0.000186 ***
## apoe.reg2     0.835986   0.109611   7.627 2.41e-14 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1459.5  on 1061  degrees of freedom
## Residual deviance: 1310.6  on 1057  degrees of freedom
## AIC: 1320.6
## 
## Number of Fisher Scoring iterations: 4
Odds ratio
## Attente de la réalisation du profilage...
##                     OR     2.5 % 97.5 %         p    
## (Intercept)  0.0050016 0.0013833 0.0174 < 2.2e-16 ***
## glucose_jeun 0.9814492 0.9119392 1.0571 0.6167817    
## age          1.0661432 1.0502179 1.0828 < 2.2e-16 ***
## sexe         1.6634760 1.2748621 2.1748 0.0001864 ***
## apoe.reg2    2.3070884 1.8666250 2.8695 2.405e-14 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Modèle 4

Modèle
## 
## Call:
## glm(formula = taut2 ~ glucose_jeun + age + sexe + apoe.reg2 + 
##     ch + dia, family = binomial(logit), data = data_tm_g)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.0772  -1.0711   0.6329   0.9878   2.0364  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -5.387699   0.655519  -8.219  < 2e-16 ***
## glucose_jeun -0.017048   0.041537  -0.410 0.681484    
## age           0.065620   0.007919   8.287  < 2e-16 ***
## sexe          0.510101   0.136270   3.743 0.000182 ***
## apoe.reg2     0.840498   0.109982   7.642 2.14e-14 ***
## ch           -0.222286   0.188180  -1.181 0.237508    
## dia           0.015966   0.273484   0.058 0.953445    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1459.5  on 1061  degrees of freedom
## Residual deviance: 1309.2  on 1055  degrees of freedom
## AIC: 1323.2
## 
## Number of Fisher Scoring iterations: 4
Odds ratio
## Attente de la réalisation du profilage...
##                     OR     2.5 % 97.5 %         p    
## (Intercept)  0.0045725 0.0012383 0.0162 < 2.2e-16 ***
## glucose_jeun 0.9830963 0.9061876 1.0676 0.6814844    
## age          1.0678209 1.0516056 1.0848 < 2.2e-16 ***
## sexe         1.6654596 1.2761794 2.1778 0.0001816 ***
## apoe.reg2    2.3175199 1.8736955 2.8845 2.136e-14 ***
## ch           0.8006864 0.5538763 1.1593 0.2375080    
## dia          1.0160943 0.5943009 1.7412 0.9534454    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Modèle 5

Modèle
## 
## Call:
## glm(formula = taut2 ~ glucose_jeun + age + sexe + apoe.reg2 + 
##     ch + dia + mmse, family = binomial(logit), data = data_tm_g)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.2921  -1.0301   0.5591   0.9620   2.0668  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -3.15206    0.74252  -4.245 2.19e-05 ***
## glucose_jeun -0.02655    0.04209  -0.631  0.52821    
## age           0.06540    0.00812   8.054 8.03e-16 ***
## sexe          0.44616    0.13936   3.201  0.00137 ** 
## apoe.reg2     0.77796    0.11147   6.979 2.97e-12 ***
## ch           -0.20651    0.19132  -1.079  0.28041    
## dia           0.02985    0.27861   0.107  0.91468    
## mmse         -0.08995    0.01410  -6.377 1.80e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1459.5  on 1061  degrees of freedom
## Residual deviance: 1265.1  on 1054  degrees of freedom
## AIC: 1281.1
## 
## Number of Fisher Scoring iterations: 3
Odds ratio
## Attente de la réalisation du profilage...
##                     OR     2.5 % 97.5 %         p    
## (Intercept)  0.0427641 0.0098501 0.1815 2.185e-05 ***
## glucose_jeun 0.9738012 0.8968111 1.0589  0.528212    
## age          1.0675838 1.0509627 1.0850 8.033e-16 ***
## sexe         1.5623053 1.1896561 2.0550  0.001367 ** 
## apoe.reg2    2.1770350 1.7545592 2.7171 2.971e-12 ***
## ch           0.8134203 0.5592232 1.1850  0.280407    
## dia          1.0302988 0.5968231 1.7841  0.914681    
## mmse         0.9139769 0.8886699 0.9392 1.802e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Modèle 6

Modèle
## 
## Call:
## glm(formula = taut2 ~ glucose_jeun + age + sexe + apoe.reg2 + 
##     ch + dia + mmse + imc, family = binomial(logit), data = data_tm_g)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.3933  -0.9979   0.5032   0.9449   2.1521  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -1.058634   0.860332  -1.230  0.21851    
## glucose_jeun  0.014078   0.044227   0.318  0.75024    
## age           0.063439   0.008246   7.694 1.43e-14 ***
## sexe          0.415079   0.140989   2.944  0.00324 ** 
## apoe.reg2     0.734846   0.112953   6.506 7.73e-11 ***
## ch           -0.137548   0.194421  -0.707  0.47927    
## dia           0.067999   0.282772   0.240  0.80996    
## mmse         -0.092277   0.014268  -6.468 9.96e-11 ***
## imc          -0.083022   0.017256  -4.811 1.50e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1459.5  on 1061  degrees of freedom
## Residual deviance: 1240.9  on 1053  degrees of freedom
## AIC: 1258.9
## 
## Number of Fisher Scoring iterations: 3
Odds ratio
## Attente de la réalisation du profilage...
##                    OR    2.5 % 97.5 %         p    
## (Intercept)  0.346929 0.063946 1.8707  0.218512    
## glucose_jeun 1.014178 0.930789 1.1078  0.750244    
## age          1.065495 1.048645 1.0831 1.429e-14 ***
## sexe         1.514491 1.149463 1.9983  0.003239 ** 
## apoe.reg2    2.085161 1.675472 2.6098 7.729e-11 ***
## ch           0.871493 0.595617 1.2776  0.479272    
## dia          1.070364 0.614933 1.8685  0.809963    
## mmse         0.911853 0.886304 0.9373 9.959e-11 ***
## imc          0.920331 0.889359 0.9517 1.500e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Tableau récapitulatif

Characteristic Modèle 1 Modèle 2 Modèle 3 Modèle 4 Modèle 5 Modèle 6
OR1 95% CI1 p-value OR1 95% CI1 p-value OR1 95% CI1 p-value OR1 95% CI1 p-value OR1 95% CI1 p-value OR1 95% CI1 p-value
glucose_jeun 0.96 0.90, 1.03 0.3 0.99 0.92, 1.07 0.8 0.98 0.91, 1.06 0.6 0.98 0.91, 1.07 0.7 0.97 0.90, 1.06 0.5 1.01 0.93, 1.11 0.8
age 1.06 1.05, 1.08 <0.001 1.06 1.05, 1.08 <0.001 1.07 1.05, 1.08 <0.001 1.07 1.05, 1.08 <0.001 1.07 1.05, 1.08 <0.001 1.07 1.05, 1.08 <0.001
sexe 1.68 1.30, 2.18 <0.001 1.66 1.27, 2.17 <0.001 1.67 1.28, 2.18 <0.001 1.56 1.19, 2.05 0.001 1.51 1.15, 2.00 0.003
apoe.reg2 2.31 1.87, 2.87 <0.001 2.32 1.87, 2.88 <0.001 2.18 1.75, 2.72 <0.001 2.09 1.68, 2.61 <0.001
ch 0.80 0.55, 1.16 0.2 0.81 0.56, 1.18 0.3 0.87 0.60, 1.28 0.5
dia 1.02 0.59, 1.74 >0.9 1.03 0.60, 1.78 >0.9 1.07 0.61, 1.87 0.8
mmse 0.91 0.89, 0.94 <0.001 0.91 0.89, 0.94 <0.001
imc 0.92 0.89, 0.95 <0.001
1 OR = Odds Ratio, CI = Confidence Interval

Modèles sur la Triglycéride

Légende
## [1] "N = 962"

Modèle 1 : Triglycéride + age
Modèle 2 : Triglycéride + age + sexe
Modèle 3 : Triglycéride + age + sexe + APOE
Modèle 4 : Triglycéride + age + sexe + APOE + Traitements
Modèle 5 : Triglycéride + age + sexe + APOE + Traitements + MMSE
Modèle 6 : Triglycéride + age + sexe + APOE + Traitements + MMSE + IMC

Modèle 1

Modèle
## 
## Call:
## glm(formula = taut2 ~ triglycerides + age, family = binomial(logit), 
##     data = data_tm_t)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.7970  -1.2131   0.7845   1.0419   1.7015  
## 
## Coefficients:
##                Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   -3.447982   0.574677  -6.000 1.97e-09 ***
## triglycerides -0.388536   0.189244  -2.053   0.0401 *  
## age            0.058717   0.007851   7.479 7.50e-14 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1318.1  on 961  degrees of freedom
## Residual deviance: 1253.0  on 959  degrees of freedom
## AIC: 1259
## 
## Number of Fisher Scoring iterations: 4
Odds ratio
## Attente de la réalisation du profilage...
##                     OR    2.5 % 97.5 %         p    
## (Intercept)   0.031810 0.010172 0.0970 1.975e-09 ***
## triglycerides 0.678049 0.467221 0.9818   0.04006 *  
## age           1.060475 1.044485 1.0772 7.499e-14 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Modèle 2

Modèle
## 
## Call:
## glm(formula = taut2 ~ triglycerides + age + sexe, family = binomial(logit), 
##     data = data_tm_t)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -1.935  -1.178   0.752   1.021   1.807  
## 
## Coefficients:
##                Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   -4.568139   0.647516  -7.055 1.73e-12 ***
## triglycerides -0.339848   0.191594  -1.774   0.0761 .  
## age            0.061632   0.007991   7.713 1.23e-14 ***
## sexe           0.567849   0.137217   4.138 3.50e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1318.1  on 961  degrees of freedom
## Residual deviance: 1235.6  on 958  degrees of freedom
## AIC: 1243.6
## 
## Number of Fisher Scoring iterations: 4
Odds ratio
## Attente de la réalisation du profilage...
##                      OR     2.5 % 97.5 %         p    
## (Intercept)   0.0103773 0.0028633 0.0363 1.728e-12 ***
## triglycerides 0.7118782 0.4883272 1.0357    0.0761 .  
## age           1.0635707 1.0472623 1.0806 1.231e-14 ***
## sexe          1.7644671 1.3496249 2.3117 3.499e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Modèle 3

Modèle
## 
## Call:
## glm(formula = taut2 ~ triglycerides + age + sexe + apoe.reg2, 
##     family = binomial(logit), data = data_tm_t)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.0845  -1.0719   0.6276   0.9747   2.0244  
## 
## Coefficients:
##                Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   -5.142770   0.674758  -7.622 2.50e-14 ***
## triglycerides -0.367371   0.198484  -1.851   0.0642 .  
## age            0.064078   0.008262   7.756 8.78e-15 ***
## sexe           0.561915   0.141717   3.965 7.34e-05 ***
## apoe.reg2      0.868569   0.116071   7.483 7.26e-14 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1318.1  on 961  degrees of freedom
## Residual deviance: 1173.8  on 957  degrees of freedom
## AIC: 1183.8
## 
## Number of Fisher Scoring iterations: 4
Odds ratio
## Attente de la réalisation du profilage...
##                      OR     2.5 % 97.5 %         p    
## (Intercept)   0.0058415 0.0015234 0.0215 2.505e-14 ***
## triglycerides 0.6925525 0.4684666 1.0208   0.06419 .  
## age           1.0661754 1.0492939 1.0839 8.779e-15 ***
## sexe          1.7540276 1.3299148 2.3186 7.338e-05 ***
## apoe.reg2     2.3834970 1.9048714 3.0035 7.259e-14 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Modèle 4

Modèle
## 
## Call:
## glm(formula = taut2 ~ triglycerides + age + sexe + apoe.reg2 + 
##     ch + dia, family = binomial(logit), data = data_tm_t)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.1091  -1.0677   0.6217   0.9671   2.0283  
## 
## Coefficients:
##                Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   -5.221243   0.679373  -7.685 1.53e-14 ***
## triglycerides -0.375945   0.199391  -1.885   0.0594 .  
## age            0.065659   0.008409   7.808 5.79e-15 ***
## sexe           0.564857   0.142488   3.964 7.36e-05 ***
## apoe.reg2      0.870242   0.116061   7.498 6.47e-14 ***
## ch            -0.238033   0.203516  -1.170   0.2422    
## dia            0.082530   0.268912   0.307   0.7589    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1318.1  on 961  degrees of freedom
## Residual deviance: 1172.5  on 955  degrees of freedom
## AIC: 1186.5
## 
## Number of Fisher Scoring iterations: 4
Odds ratio
## Attente de la réalisation du profilage...
##                      OR     2.5 % 97.5 %         p    
## (Intercept)   0.0054006 0.0013953 0.0201 1.525e-14 ***
## triglycerides 0.6866401 0.4636211 1.0138   0.05937 .  
## age           1.0678624 1.0506600 1.0859 5.790e-15 ***
## sexe          1.7591956 1.3318415 2.3290 7.363e-05 ***
## apoe.reg2     2.3874891 1.9080557 3.0084 6.472e-14 ***
## ch            0.7881769 0.5292142 1.1766   0.24216    
## dia           1.0860317 0.6421532 1.8489   0.75892    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Modèle 5

Modèle
## 
## Call:
## glm(formula = taut2 ~ triglycerides + age + sexe + apoe.reg2 + 
##     ch + dia + mmse, family = binomial(logit), data = data_tm_t)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.3800  -1.0147   0.5313   0.9458   2.1547  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   -2.72841    0.77960  -3.500 0.000466 ***
## triglycerides -0.46211    0.20339  -2.272 0.023087 *  
## age            0.06562    0.00867   7.569 3.77e-14 ***
## sexe           0.51551    0.14618   3.527 0.000421 ***
## apoe.reg2      0.78521    0.11790   6.660 2.74e-11 ***
## ch            -0.23256    0.20783  -1.119 0.263152    
## dia            0.08998    0.27700   0.325 0.745312    
## mmse          -0.10112    0.01528  -6.617 3.65e-11 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1318.1  on 961  degrees of freedom
## Residual deviance: 1124.3  on 954  degrees of freedom
## AIC: 1140.3
## 
## Number of Fisher Scoring iterations: 3
Odds ratio
## Attente de la réalisation du profilage...
##                     OR    2.5 % 97.5 %         p    
## (Intercept)   0.065323 0.014017 0.2987 0.0004656 ***
## triglycerides 0.629954 0.421984 0.9374 0.0230867 *  
## age           1.067822 1.050093 1.0864 3.771e-14 ***
## sexe          1.674497 1.258364 2.2327 0.0004210 ***
## apoe.reg2     2.192862 1.745586 2.7724 2.740e-11 ***
## ch            0.792504 0.527599 1.1931 0.2631522    
## dia           1.094149 0.636707 1.8922 0.7453116    
## mmse          0.903824 0.876684 0.9309 3.655e-11 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Modèle 6

Modèle
## 
## Call:
## glm(formula = taut2 ~ triglycerides + age + sexe + apoe.reg2 + 
##     ch + dia + mmse + imc, family = binomial(logit), data = data_tm_t)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.4733  -0.9876   0.4984   0.9145   2.2043  
## 
## Coefficients:
##                Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   -0.637392   0.903813  -0.705  0.48067    
## triglycerides -0.322749   0.207873  -1.553  0.12051    
## age            0.065273   0.008837   7.386 1.51e-13 ***
## sexe           0.465527   0.148107   3.143  0.00167 ** 
## apoe.reg2      0.750311   0.119546   6.276 3.47e-10 ***
## ch            -0.171779   0.210855  -0.815  0.41526    
## dia            0.220226   0.282967   0.778  0.43641    
## mmse          -0.102425   0.015413  -6.645 3.03e-11 ***
## imc           -0.083872   0.018261  -4.593 4.37e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1318.1  on 961  degrees of freedom
## Residual deviance: 1102.3  on 953  degrees of freedom
## AIC: 1120.3
## 
## Number of Fisher Scoring iterations: 4
Odds ratio
## Attente de la réalisation du profilage...
##                     OR    2.5 % 97.5 %         p    
## (Intercept)   0.528669 0.089739 3.1149  0.480670    
## triglycerides 0.724156 0.481135 1.0878  0.120513    
## age           1.067450 1.049385 1.0864 1.511e-13 ***
## sexe          1.592853 1.192279 2.1315  0.001671 ** 
## apoe.reg2     2.117658 1.680238 2.6858 3.467e-10 ***
## ch            0.842165 0.557440 1.2755  0.415257    
## dia           1.246358 0.717662 2.1832  0.436408    
## mmse          0.902646 0.875307 0.9299 3.027e-11 ***
## imc           0.919549 0.886811 0.9527 4.371e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Tableau récapitulatif

Characteristic Modèle 1 Modèle 2 Modèle 3 Modèle 4 Modèle 5 Modèle 6
OR1 95% CI1 p-value OR1 95% CI1 p-value OR1 95% CI1 p-value OR1 95% CI1 p-value OR1 95% CI1 p-value OR1 95% CI1 p-value
triglycerides 0.68 0.47, 0.98 0.040 0.71 0.49, 1.04 0.076 0.69 0.47, 1.02 0.064 0.69 0.46, 1.01 0.059 0.63 0.42, 0.94 0.023 0.72 0.48, 1.09 0.12
age 1.06 1.04, 1.08 <0.001 1.06 1.05, 1.08 <0.001 1.07 1.05, 1.08 <0.001 1.07 1.05, 1.09 <0.001 1.07 1.05, 1.09 <0.001 1.07 1.05, 1.09 <0.001
sexe 1.76 1.35, 2.31 <0.001 1.75 1.33, 2.32 <0.001 1.76 1.33, 2.33 <0.001 1.67 1.26, 2.23 <0.001 1.59 1.19, 2.13 0.002
apoe.reg2 2.38 1.90, 3.00 <0.001 2.39 1.91, 3.01 <0.001 2.19 1.75, 2.77 <0.001 2.12 1.68, 2.69 <0.001
ch 0.79 0.53, 1.18 0.2 0.79 0.53, 1.19 0.3 0.84 0.56, 1.28 0.4
dia 1.09 0.64, 1.85 0.8 1.09 0.64, 1.89 0.7 1.25 0.72, 2.18 0.4
mmse 0.90 0.88, 0.93 <0.001 0.90 0.88, 0.93 <0.001
imc 0.92 0.89, 0.95 <0.001
1 OR = Odds Ratio, CI = Confidence Interval

Modèles sur le Cholestérol

Légende
## [1] "N = 1006"

Modèle 1 : Cholestérol + age
Modèle 2 : Cholestérol + age + sexe
Modèle 3 : Cholestérol + age + sexe + APOE
Modèle 4 : Cholestérol + age + sexe + APOE + Traitements
Modèle 5 : Cholestérol + age + sexe + APOE + Traitements + MMSE
Modèle 6 : Cholestérol + age + sexe + APOE + Traitements + MMSE + IMC

Modèle 1

Modèle
## 
## Call:
## glm(formula = taut2 ~ cholesterol_total + age, family = binomial(logit), 
##     data = data_tm_c)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.9232  -1.1860   0.7911   1.0419   1.6832  
## 
## Coefficients:
##                    Estimate Std. Error z value Pr(>|z|)    
## (Intercept)       -4.660714   0.647808  -7.195 6.27e-13 ***
## cholesterol_total  0.307506   0.133425   2.305   0.0212 *  
## age                0.061145   0.007735   7.905 2.67e-15 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1380.7  on 1005  degrees of freedom
## Residual deviance: 1311.0  on 1003  degrees of freedom
## AIC: 1317
## 
## Number of Fisher Scoring iterations: 4
Odds ratio
## Attente de la réalisation du profilage...
##                          OR     2.5 % 97.5 %         p    
## (Intercept)       0.0094597 0.0026040 0.0331 6.265e-13 ***
## cholesterol_total 1.3600295 1.0509789 1.7738   0.02118 *  
## age               1.0630526 1.0472631 1.0795 2.671e-15 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Modèle 2

Modèle
## 
## Call:
## glm(formula = taut2 ~ cholesterol_total + age + sexe, family = binomial(logit), 
##     data = data_tm_c)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -1.915  -1.171   0.747   1.033   1.789  
## 
## Coefficients:
##                    Estimate Std. Error z value Pr(>|z|)    
## (Intercept)       -5.213730   0.670521  -7.776 7.51e-15 ***
## cholesterol_total  0.141583   0.139622   1.014 0.310559    
## age                0.062791   0.007821   8.029 9.86e-16 ***
## sexe               0.520688   0.140957   3.694 0.000221 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1380.7  on 1005  degrees of freedom
## Residual deviance: 1297.2  on 1002  degrees of freedom
## AIC: 1305.2
## 
## Number of Fisher Scoring iterations: 4
Odds ratio
## Attente de la réalisation du profilage...
##                          OR     2.5 % 97.5 %         p    
## (Intercept)       0.0054413 0.0014307 0.0199 7.507e-15 ***
## cholesterol_total 1.1520963 0.8789710 1.5207 0.3105593    
## age               1.0648038 1.0488198 1.0815 9.863e-16 ***
## sexe              1.6831846 1.2780364 2.2215 0.0002208 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Modèle 3

Modèle
## 
## Call:
## glm(formula = taut2 ~ cholesterol_total + age + sexe + apoe.reg2, 
##     family = binomial(logit), data = data_tm_c)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.0700  -1.0748   0.6220   0.9793   1.9914  
## 
## Coefficients:
##                    Estimate Std. Error z value Pr(>|z|)    
## (Intercept)       -5.649346   0.694910  -8.130 4.31e-16 ***
## cholesterol_total  0.049765   0.141842   0.351 0.725702    
## age                0.065037   0.008087   8.042 8.81e-16 ***
## sexe               0.542792   0.145418   3.733 0.000189 ***
## apoe.reg2          0.875663   0.114193   7.668 1.74e-14 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1380.7  on 1005  degrees of freedom
## Residual deviance: 1232.3  on 1001  degrees of freedom
## AIC: 1242.3
## 
## Number of Fisher Scoring iterations: 4
Odds ratio
## Attente de la réalisation du profilage...
##                           OR      2.5 % 97.5 %         p    
## (Intercept)       0.00351982 0.00088062 0.0135 4.307e-16 ***
## cholesterol_total 1.05102417 0.79646116 1.3921 0.7257016    
## age               1.06719872 1.05065308 1.0845 8.813e-16 ***
## sexe              1.72080548 1.29536480 2.2914 0.0001895 ***
## apoe.reg2         2.40046625 1.92522567 3.0133 1.743e-14 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Modèle 4

Modèle
## 
## Call:
## glm(formula = taut2 ~ cholesterol_total + age + sexe + apoe.reg2 + 
##     ch + dia, family = binomial(logit), data = data_tm_c)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.1020  -1.0681   0.6216   0.9718   1.9898  
## 
## Coefficients:
##                    Estimate Std. Error z value Pr(>|z|)    
## (Intercept)       -5.713135   0.699717  -8.165 3.22e-16 ***
## cholesterol_total  0.029640   0.143704   0.206 0.836590    
## age                0.066972   0.008216   8.152 3.59e-16 ***
## sexe               0.549691   0.145812   3.770 0.000163 ***
## apoe.reg2          0.880076   0.114262   7.702 1.34e-14 ***
## ch                -0.297787   0.196708  -1.514 0.130062    
## dia                0.057587   0.259896   0.222 0.824642    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1380.7  on 1005  degrees of freedom
## Residual deviance: 1230.1  on  999  degrees of freedom
## AIC: 1244.1
## 
## Number of Fisher Scoring iterations: 4
Odds ratio
## Attente de la réalisation du profilage...
##                           OR      2.5 % 97.5 %         p    
## (Intercept)       0.00330230 0.00081814 0.0127 3.216e-16 ***
## cholesterol_total 1.03008375 0.77741091 1.3690 0.8365895    
## age               1.06926569 1.05242906 1.0869 3.594e-16 ***
## sexe              1.73271711 1.30333006 2.3090 0.0001633 ***
## apoe.reg2         2.41108347 1.93345772 3.0270 1.337e-14 ***
## ch                0.74245915 0.50489384 1.0928 0.1300622    
## dia               1.05927791 0.63691485 1.7694 0.8246417    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Modèle 5

Modèle
## 
## Call:
## glm(formula = taut2 ~ cholesterol_total + age + sexe + apoe.reg2 + 
##     ch + dia + mmse, family = binomial(logit), data = data_tm_c)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.3359  -1.0154   0.5441   0.9402   2.1084  
## 
## Coefficients:
##                    Estimate Std. Error z value Pr(>|z|)    
## (Intercept)       -3.495554   0.778888  -4.488 7.19e-06 ***
## cholesterol_total  0.074160   0.146956   0.505  0.61381    
## age                0.067420   0.008462   7.968 1.61e-15 ***
## sexe               0.480575   0.149629   3.212  0.00132 ** 
## apoe.reg2          0.797070   0.115989   6.872 6.33e-12 ***
## ch                -0.280416   0.200623  -1.398  0.16219    
## dia                0.045243   0.266505   0.170  0.86520    
## mmse              -0.097057   0.014862  -6.531 6.55e-11 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1380.7  on 1005  degrees of freedom
## Residual deviance: 1183.5  on  998  degrees of freedom
## AIC: 1199.5
## 
## Number of Fisher Scoring iterations: 3
Odds ratio
## Attente de la réalisation du profilage...
##                          OR     2.5 % 97.5 %         p    
## (Intercept)       0.0303320 0.0064902 0.1379 7.194e-06 ***
## cholesterol_total 1.0769794 0.8079718 1.4413  0.613811    
## age               1.0697447 1.0524064 1.0879 1.615e-15 ***
## sexe              1.6170038 1.2068952 2.1705  0.001319 ** 
## apoe.reg2         2.2190299 1.7728729 2.7946 6.333e-12 ***
## ch                0.7554693 0.5098089 1.1205  0.162194    
## dia               1.0462816 0.6210770 1.7706  0.865197    
## mmse              0.9075046 0.8810193 0.9339 6.552e-11 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Modèle 6

Modèle
## 
## Call:
## glm(formula = taut2 ~ cholesterol_total + age + sexe + apoe.reg2 + 
##     ch + dia + mmse + imc, family = binomial(logit), data = data_tm_c)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.4409  -0.9879   0.4925   0.9248   2.1827  
## 
## Coefficients:
##                    Estimate Std. Error z value Pr(>|z|)    
## (Intercept)       -1.170054   0.927097  -1.262  0.20693    
## cholesterol_total  0.014869   0.149807   0.099  0.92094    
## age                0.066031   0.008611   7.668 1.74e-14 ***
## sexe               0.443433   0.151478   2.927  0.00342 ** 
## apoe.reg2          0.764500   0.117424   6.511 7.49e-11 ***
## ch                -0.232096   0.203499  -1.141  0.25407    
## dia                0.160569   0.271672   0.591  0.55449    
## mmse              -0.098491   0.015002  -6.565 5.19e-11 ***
## imc               -0.080280   0.017480  -4.593 4.38e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1380.7  on 1005  degrees of freedom
## Residual deviance: 1161.5  on  997  degrees of freedom
## AIC: 1179.5
## 
## Number of Fisher Scoring iterations: 4
Odds ratio
## Attente de la réalisation du profilage...
##                         OR    2.5 % 97.5 %         p    
## (Intercept)       0.310350 0.050153 1.9073  0.206927    
## cholesterol_total 1.014980 0.757085 1.3655  0.920935    
## age               1.068259 1.050633 1.0867 1.742e-14 ***
## sexe              1.558046 1.158526 2.0987  0.003418 ** 
## apoe.reg2         2.147921 1.711151 2.7125 7.485e-11 ***
## ch                0.792870 0.532107 1.1828  0.254067    
## dia               1.174179 0.690688 2.0095  0.554493    
## mmse              0.906204 0.879506 0.9328 5.189e-11 ***
## imc               0.922858 0.891390 0.9547 4.377e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Tableau récapitulatif

Characteristic Modèle 1 Modèle 2 Modèle 3 Modèle 4 Modèle 5 Modèle 6
OR1 95% CI1 p-value OR1 95% CI1 p-value OR1 95% CI1 p-value OR1 95% CI1 p-value OR1 95% CI1 p-value OR1 95% CI1 p-value
glucose_jeun 0.96 0.90, 1.03 0.3 0.99 0.92, 1.07 0.8 0.98 0.91, 1.06 0.6 0.98 0.91, 1.07 0.7 0.97 0.90, 1.06 0.5 1.01 0.93, 1.11 0.8
age 1.06 1.05, 1.08 <0.001 1.06 1.05, 1.08 <0.001 1.07 1.05, 1.08 <0.001 1.07 1.05, 1.08 <0.001 1.07 1.05, 1.08 <0.001 1.07 1.05, 1.08 <0.001
sexe 1.68 1.30, 2.18 <0.001 1.66 1.27, 2.17 <0.001 1.67 1.28, 2.18 <0.001 1.56 1.19, 2.05 0.001 1.51 1.15, 2.00 0.003
apoe.reg2 2.31 1.87, 2.87 <0.001 2.32 1.87, 2.88 <0.001 2.18 1.75, 2.72 <0.001 2.09 1.68, 2.61 <0.001
ch 0.80 0.55, 1.16 0.2 0.81 0.56, 1.18 0.3 0.87 0.60, 1.28 0.5
dia 1.02 0.59, 1.74 >0.9 1.03 0.60, 1.78 >0.9 1.07 0.61, 1.87 0.8
mmse 0.91 0.89, 0.94 <0.001 0.91 0.89, 0.94 <0.001
imc 0.92 0.89, 0.95 <0.001
1 OR = Odds Ratio, CI = Confidence Interval

Tau en continu

Nombre d’Observations dans les modèles

## [1] "Modèle"
## [1] " + tau_"
## [1] "N = 1722"
## [1] " + glucose_jeun"
## [1] "N = 1501"
## [1] " + triglycerides"
## [1] "N = 1281"
## [1] " + cholesterol_total"
## [1] "N = 1280"
## [1] " + age"
## [1] "N = 1280"
## [1] " + sexe"
## [1] "N = 1280"
## [1] " + apoe.reg2"
## [1] "N = 1176"
## [1] " + ch"
## [1] "N = 1176"
## [1] " + mmse"
## [1] "N = 1098"
## [1] " + dia"
## [1] "N = 1098"
## [1] " + imc"
## [1] "N = 934"

Matrice de corrélation

Modèles sur le Glucose

Légende
## [1] "N = 1061"

Modèle 1 : Glucose + age
Modèle 2 : Glucose + age + sexe
Modèle 3 : Glucose + age + sexe + APOE
Modèle 4 : Glucose + age + sexe + APOE + Traitements
Modèle 5 : Glucose + age + sexe + APOE + Traitements + MMSE
Modèle 6 : Glucose + age + sexe + APOE + Traitements + MMSE + IMC

Modèle 1

Modèle
## 
## Call:
## lm(formula = tau_ ~ glucose_jeun + age, data = data_tc_g)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.3354 -0.6850 -0.2529  0.4050  3.8102 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  -0.358451   0.237168  -1.511   0.1310  
## glucose_jeun -0.013720   0.016476  -0.833   0.4052  
## age           0.006052   0.003224   1.877   0.0608 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9501 on 1058 degrees of freedom
## Multiple R-squared:  0.003741,   Adjusted R-squared:  0.001858 
## F-statistic: 1.987 on 2 and 1058 DF,  p-value: 0.1377
Scatter plot

Régression linéaire
## Warning in abline(tau_glucose.mod, col = "red"): utilisation des deux premiers
## des 3 coefficients de régression

Modèle 2

Modèle
## 
## Call:
## lm(formula = tau_ ~ glucose_jeun + age + sexe, data = data_tc_g)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.3861 -0.6907 -0.2338  0.4097  3.8587 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  -0.607614   0.266579  -2.279   0.0228 *
## glucose_jeun -0.007215   0.016759  -0.431   0.6669  
## age           0.006424   0.003224   1.992   0.0466 *
## sexe          0.121337   0.059607   2.036   0.0420 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9487 on 1057 degrees of freedom
## Multiple R-squared:  0.007632,   Adjusted R-squared:  0.004815 
## F-statistic:  2.71 on 3 and 1057 DF,  p-value: 0.04396
Scatter plot

Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 4
## coefficients de régression

Modèle 3

Modèle
## 
## Call:
## lm(formula = tau_ ~ glucose_jeun + age + sexe + apoe.reg2, data = data_tc_g)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.4206 -0.6885 -0.2479  0.4240  3.8212 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  -0.631759   0.266775  -2.368   0.0181 *
## glucose_jeun -0.007835   0.016750  -0.468   0.6400  
## age           0.006343   0.003222   1.968   0.0493 *
## sexe          0.118110   0.059592   1.982   0.0477 *
## apoe.reg2     0.073792   0.045081   1.637   0.1020  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9479 on 1056 degrees of freedom
## Multiple R-squared:  0.01014,    Adjusted R-squared:  0.006394 
## F-statistic: 2.705 on 4 and 1056 DF,  p-value: 0.0292
Scatter plot

Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 5
## coefficients de régression

Modèle 4

Modèle
## 
## Call:
## lm(formula = tau_ ~ glucose_jeun + age + sexe + apoe.reg2 + ch + 
##     dia, data = data_tc_g)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.4198 -0.6862 -0.2462  0.4220  3.8228 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  -0.621889   0.270537  -2.299   0.0217 *
## glucose_jeun -0.009594   0.018346  -0.523   0.6011  
## age           0.006298   0.003271   1.925   0.0544 .
## sexe          0.118187   0.059648   1.981   0.0478 *
## apoe.reg2     0.074488   0.045313   1.644   0.1005  
## ch            0.005433   0.082812   0.066   0.9477  
## dia           0.027684   0.122759   0.226   0.8216  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9488 on 1054 degrees of freedom
## Multiple R-squared:  0.0102, Adjusted R-squared:  0.004566 
## F-statistic:  1.81 on 6 and 1054 DF,  p-value: 0.09388
Scatter plot

Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 7
## coefficients de régression

Modèle 5

Modèle
## 
## Call:
## lm(formula = tau_ ~ glucose_jeun + age + sexe + apoe.reg2 + ch + 
##     dia + mmse, data = data_tc_g)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.3997 -0.6782 -0.2538  0.4075  3.7472 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  -0.241859   0.310663  -0.779   0.4364  
## glucose_jeun -0.010997   0.018310  -0.601   0.5482  
## age           0.005833   0.003268   1.785   0.0746 .
## sexe          0.105672   0.059719   1.769   0.0771 .
## apoe.reg2     0.059700   0.045598   1.309   0.1907  
## ch            0.011314   0.082647   0.137   0.8911  
## dia           0.030215   0.122467   0.247   0.8052  
## mmse         -0.013959   0.005651  -2.470   0.0137 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9465 on 1053 degrees of freedom
## Multiple R-squared:  0.0159, Adjusted R-squared:  0.00936 
## F-statistic: 2.431 on 7 and 1053 DF,  p-value: 0.01793
Scatter plot

Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 8
## coefficients de régression

Modèle 6

Modèle
## 
## Call:
## lm(formula = tau_ ~ glucose_jeun + age + sexe + apoe.reg2 + ch + 
##     dia + mmse + imc, data = data_tc_g)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.4564 -0.6647 -0.2420  0.4117  3.7247 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   0.213477   0.362795   0.588   0.5564  
## glucose_jeun -0.001976   0.018646  -0.106   0.9156  
## age           0.005037   0.003278   1.537   0.1247  
## sexe          0.096553   0.059702   1.617   0.1061  
## apoe.reg2     0.048024   0.045750   1.050   0.2941  
## ch            0.024566   0.082640   0.297   0.7663  
## dia           0.041281   0.122273   0.338   0.7357  
## mmse         -0.014116   0.005639  -2.503   0.0125 *
## imc          -0.017243   0.007140  -2.415   0.0159 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9443 on 1052 degrees of freedom
## Multiple R-squared:  0.02133,    Adjusted R-squared:  0.01388 
## F-statistic: 2.866 on 8 and 1052 DF,  p-value: 0.003709
Scatter plot

Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 9
## coefficients de régression

Tableau récapitulatif

Model 1Model 2Model 3Model 4Model 5Model 6
(Intercept)-0.02 -0.08  -0.08  -0.08  -0.08  -0.08  
[-0.08, 0.04],(0.54)[-0.17, 0.00],(0.06) [-0.16, 0.00],(0.06) [-0.17, 0.01],(0.07) [-0.17, 0.01],(0.09) [-0.16, 0.01],(0.09) 
glucose_jeun-0.02 -0.01  -0.01  -0.02  -0.02  -0.00  
[-0.08, 0.03],(0.41)[-0.07, 0.05],(0.67) [-0.07, 0.04],(0.64) [-0.08, 0.05],(0.60) [-0.08, 0.04],(0.55) [-0.07, 0.06],(0.92) 
age0.05 0.06 *0.06 *0.06  0.05  0.05  
[-0.00, 0.11],(0.06)[0.00, 0.12],(0.05) [0.00, 0.12],(0.05) [-0.00, 0.12],(0.05) [-0.01, 0.11],(0.07) [-0.01, 0.10],(0.12) 
sexe    0.12 *0.12 *0.12 *0.11  0.10  
    [0.00, 0.24],(0.04) [0.00, 0.24],(0.05) [0.00, 0.24],(0.05) [-0.01, 0.22],(0.08) [-0.02, 0.21],(0.11) 
apoe.reg2         0.05  0.05  0.04  0.03  
         [-0.01, 0.10],(0.10) [-0.01, 0.11],(0.10) [-0.02, 0.10],(0.19) [-0.03, 0.09],(0.29) 
ch              0.01  0.01  0.02  
              [-0.16, 0.17],(0.95) [-0.15, 0.17],(0.89) [-0.14, 0.19],(0.77) 
dia              0.03  0.03  0.04  
              [-0.21, 0.27],(0.82) [-0.21, 0.27],(0.81) [-0.20, 0.28],(0.74) 
mmse                   -0.07 *-0.07 *
                   [-0.13, -0.01],(0.01) [-0.13, -0.02],(0.01) 
imc                        -0.07 *
                        [-0.13, -0.01],(0.02) 
N1061    1061     1061     1061     1061     1061     
R20.00 0.01  0.01  0.01  0.02  0.02  
All continuous predictors are mean-centered and scaled by 1 standard deviation. *** p < 0.001; ** p < 0.01; * p < 0.05.

Modèles sur la Triglycéride

Légende
## [1] "N = 961"

Modèle 1 : Triglycéride + age
Modèle 2 : Triglycéride + age + sexe
Modèle 3 : Triglycéride + age + sexe + APOE
Modèle 4 : Triglycéride + age + sexe + APOE + Traitements
Modèle 5 : Triglycéride + age + sexe + APOE + Traitements + MMSE
Modèle 6 : Triglycéride + age + sexe + APOE + Traitements + MMSE + IMC

Modèle 1

Modèle
## 
## Call:
## lm(formula = tau_ ~ triglycerides + age, data = data_tc_t)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.3294 -0.6933 -0.2524  0.4246  3.8024 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)
## (Intercept)   -0.290063   0.256226  -1.132    0.258
## triglycerides -0.011627   0.086562  -0.134    0.893
## age            0.004173   0.003430   1.217    0.224
## 
## Residual standard error: 0.9543 on 958 degrees of freedom
## Multiple R-squared:  0.001567,   Adjusted R-squared:  -0.000517 
## F-statistic: 0.752 on 2 and 958 DF,  p-value: 0.4717
Scatter plot

Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 3
## coefficients de régression

Modèle 2

Modèle
## 
## Call:
## lm(formula = tau_ ~ triglycerides + age + sexe, data = data_tc_t)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.3825 -0.6983 -0.2462  0.4234  3.8588 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   -0.5121989  0.2800396  -1.829   0.0677 .
## triglycerides  0.0001375  0.0866452   0.002   0.9987  
## age            0.0045282  0.0034296   1.320   0.1870  
## sexe           0.1207923  0.0619095   1.951   0.0513 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9529 on 957 degrees of freedom
## Multiple R-squared:  0.005523,   Adjusted R-squared:  0.002406 
## F-statistic: 1.772 on 3 and 957 DF,  p-value: 0.1509
Scatter plot

Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 4
## coefficients de régression

Modèle 3

Modèle
## 
## Call:
## lm(formula = tau_ ~ triglycerides + age + sexe + apoe.reg2, data = data_tc_t)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.4180 -0.6987 -0.2488  0.4374  3.8198 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   -0.5425169  0.2803782  -1.935   0.0533 .
## triglycerides -0.0005452  0.0865668  -0.006   0.9950  
## age            0.0044611  0.0034267   1.302   0.1933  
## sexe           0.1168435  0.0618985   1.888   0.0594 .
## apoe.reg2      0.0789522  0.0475524   1.660   0.0972 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.952 on 956 degrees of freedom
## Multiple R-squared:  0.008383,   Adjusted R-squared:  0.004234 
## F-statistic:  2.02 on 4 and 956 DF,  p-value: 0.08955
Scatter plot

Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 5
## coefficients de régression

Modèle 4

Modèle
## 
## Call:
## lm(formula = tau_ ~ triglycerides + age + sexe + apoe.reg2 + 
##     ch + dia, data = data_tc_t)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.4195 -0.6996 -0.2475  0.4318  3.8215 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   -0.547546   0.281860  -1.943   0.0524 .
## triglycerides -0.003013   0.086965  -0.035   0.9724  
## age            0.004521   0.003480   1.299   0.1942  
## sexe           0.118480   0.062210   1.905   0.0571 .
## apoe.reg2      0.079812   0.047669   1.674   0.0944 .
## ch            -0.017019   0.089410  -0.190   0.8491  
## dia            0.038308   0.122015   0.314   0.7536  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.953 on 954 degrees of freedom
## Multiple R-squared:  0.008506,   Adjusted R-squared:  0.00227 
## F-statistic: 1.364 on 6 and 954 DF,  p-value: 0.2262
Scatter plot

Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 7
## coefficients de régression

Modèle 5

Modèle
## 
## Call:
## lm(formula = tau_ ~ triglycerides + age + sexe + apoe.reg2 + 
##     ch + dia + mmse, data = data_tc_t)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.4008 -0.6864 -0.2551  0.4471  3.7573 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   -0.198120   0.324303  -0.611   0.5414  
## triglycerides -0.010103   0.086859  -0.116   0.9074  
## age            0.004100   0.003479   1.178   0.2389  
## sexe           0.109213   0.062237   1.755   0.0796 .
## apoe.reg2      0.063762   0.048150   1.324   0.1857  
## ch            -0.012565   0.089261  -0.141   0.8881  
## dia            0.038073   0.121780   0.313   0.7546  
## mmse          -0.012989   0.005998  -2.166   0.0306 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9511 on 953 degrees of freedom
## Multiple R-squared:  0.01336,    Adjusted R-squared:  0.006114 
## F-statistic: 1.844 on 7 and 953 DF,  p-value: 0.07578
Scatter plot

Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 8
## coefficients de régression

Modèle 6

Modèle
## 
## Call:
## lm(formula = tau_ ~ triglycerides + age + sexe + apoe.reg2 + 
##     ch + dia + mmse + imc, data = data_tc_t)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.4449 -0.6810 -0.2503  0.4319  3.7275 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)  
## (Intercept)    0.172897   0.381455   0.453   0.6505  
## triglycerides  0.013891   0.087724   0.158   0.8742  
## age            0.003630   0.003484   1.042   0.2978  
## sexe           0.098761   0.062418   1.582   0.1139  
## apoe.reg2      0.055450   0.048302   1.148   0.2513  
## ch            -0.002872   0.089305  -0.032   0.9743  
## dia            0.063978   0.122439   0.523   0.6014  
## mmse          -0.012965   0.005990  -2.164   0.0307 *
## imc           -0.013847   0.007520  -1.841   0.0659 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9499 on 952 degrees of freedom
## Multiple R-squared:  0.01686,    Adjusted R-squared:  0.008601 
## F-statistic: 2.041 on 8 and 952 DF,  p-value: 0.03906
Scatter plot

Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 9
## coefficients de régression

Tableau récapitulatif

Model 1Model 2Model 3Model 4Model 5Model 6
(Intercept)-0.01 -0.08 -0.07 -0.07 -0.07  -0.08  
[-0.07, 0.05],(0.73)[-0.16, 0.01],(0.10)[-0.16, 0.02],(0.10)[-0.17, 0.02],(0.12)[-0.17, 0.02],(0.14) [-0.16, 0.01],(0.09) 
triglycerides-0.00 0.00 -0.00 -0.00 -0.00       
[-0.06, 0.06],(0.89)[-0.06, 0.06],(1.00)[-0.06, 0.06],(0.99)[-0.06, 0.06],(0.97)[-0.06, 0.06],(0.91)      
age0.04 0.04 0.04 0.04 0.04  0.05  
[-0.02, 0.10],(0.22)[-0.02, 0.10],(0.19)[-0.02, 0.10],(0.19)[-0.02, 0.10],(0.19)[-0.02, 0.10],(0.24) [-0.01, 0.10],(0.12) 
sexe    0.12 0.12 0.12 0.11  0.10  
    [-0.00, 0.24],(0.05)[-0.00, 0.24],(0.06)[-0.00, 0.24],(0.06)[-0.01, 0.23],(0.08) [-0.02, 0.21],(0.11) 
apoe.reg2        0.05 0.05 0.04  0.03  
        [-0.01, 0.11],(0.10)[-0.01, 0.11],(0.09)[-0.02, 0.10],(0.19) [-0.03, 0.09],(0.29) 
ch            -0.02 -0.01  0.02  
            [-0.19, 0.16],(0.85)[-0.19, 0.16],(0.89) [-0.14, 0.19],(0.77) 
dia            0.04 0.04  0.04  
            [-0.20, 0.28],(0.75)[-0.20, 0.28],(0.75) [-0.20, 0.28],(0.74) 
mmse                -0.07 *-0.07 *
                [-0.13, -0.01],(0.03) [-0.13, -0.02],(0.01) 
glucose_jeun                     -0.00  
                     [-0.07, 0.06],(0.92) 
imc                     -0.07 *
                     [-0.13, -0.01],(0.02) 
N961    961    961    961    961     1061     
R20.00 0.01 0.01 0.01 0.01  0.02  
All continuous predictors are mean-centered and scaled by 1 standard deviation. *** p < 0.001; ** p < 0.01; * p < 0.05.

Modèles sur la Cholestérol

Légende
## [1] "N = 1005"

Modèle 1 : Cholestérol + age
Modèle 2 : Cholestérol + age + sexe
Modèle 3 : Cholestérol + age + sexe + APOE
Modèle 4 : Cholestérol + age + sexe + APOE + Traitements
Modèle 5 : Cholestérol + age + sexe + APOE + Traitements + MMSE
Modèle 6 : Cholestérol + age + sexe + APOE + Traitements + MMSE + IMC

Modèle 1

Modèle
## 
## Call:
## lm(formula = tau_ ~ cholesterol_total + age, data = data_tc_c)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.3196 -0.6846 -0.2534  0.4241  3.8190 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)
## (Intercept)       -0.236187   0.278097  -0.849    0.396
## cholesterol_total -0.018758   0.058313  -0.322    0.748
## age                0.003721   0.003337   1.115    0.265
## 
## Residual standard error: 0.947 on 1002 degrees of freedom
## Multiple R-squared:  0.001451,   Adjusted R-squared:  -0.0005425 
## F-statistic: 0.7278 on 2 and 1002 DF,  p-value: 0.4832
Scatter plot

Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 3
## coefficients de régression

Modèle 2

Modèle
## 
## Call:
## lm(formula = tau_ ~ cholesterol_total + age + sexe, data = data_tc_c)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.3554 -0.6970 -0.2285  0.4346  3.8958 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       -0.367296   0.284120  -1.293   0.1964  
## cholesterol_total -0.061565   0.061475  -1.001   0.3168  
## age                0.003911   0.003332   1.174   0.2408  
## sexe               0.136846   0.063229   2.164   0.0307 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9452 on 1001 degrees of freedom
## Multiple R-squared:  0.006101,   Adjusted R-squared:  0.003123 
## F-statistic: 2.048 on 3 and 1001 DF,  p-value: 0.1055
Scatter plot

Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 4
## coefficients de régression

Modèle 3

Modèle
## 
## Call:
## lm(formula = tau_ ~ cholesterol_total + age + sexe + apoe.reg2, 
##     data = data_tc_c)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.3842 -0.6914 -0.2438  0.4463  3.8587 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       -0.381137   0.283880  -1.343   0.1797  
## cholesterol_total -0.072091   0.061667  -1.169   0.2427  
## age                0.003803   0.003329   1.143   0.2535  
## sexe               0.136012   0.063155   2.154   0.0315 *
## apoe.reg2          0.085714   0.046494   1.844   0.0655 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9441 on 1000 degrees of freedom
## Multiple R-squared:  0.009468,   Adjusted R-squared:  0.005506 
## F-statistic:  2.39 on 4 and 1000 DF,  p-value: 0.04929
Scatter plot

Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 5
## coefficients de régression

Modèle 4

Modèle
## 
## Call:
## lm(formula = tau_ ~ cholesterol_total + age + sexe + apoe.reg2 + 
##     ch + dia, data = data_tc_c)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.3864 -0.6918 -0.2481  0.4432  3.8565 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       -0.383799   0.285389  -1.345   0.1790  
## cholesterol_total -0.073568   0.062331  -1.180   0.2382  
## age                0.003917   0.003372   1.162   0.2457  
## sexe               0.136312   0.063358   2.151   0.0317 *
## apoe.reg2          0.086003   0.046615   1.845   0.0653 .
## ch                -0.018846   0.086090  -0.219   0.8268  
## dia                0.001231   0.116767   0.011   0.9916  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.945 on 998 degrees of freedom
## Multiple R-squared:  0.009516,   Adjusted R-squared:  0.003561 
## F-statistic: 1.598 on 6 and 998 DF,  p-value: 0.1444
Scatter plot

Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 7
## coefficients de régression

Modèle 5

Modèle
## 
## Call:
## lm(formula = tau_ ~ cholesterol_total + age + sexe + apoe.reg2 + 
##     ch + dia + mmse, data = data_tc_c)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.3690 -0.6807 -0.2439  0.4400  3.7838 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       -0.0527127  0.3205715  -0.164   0.8694  
## cholesterol_total -0.0653630  0.0623110  -1.049   0.2944  
## age                0.0035516  0.0033695   1.054   0.2921  
## sexe               0.1238271  0.0634726   1.951   0.0514 .
## apoe.reg2          0.0694395  0.0470989   1.474   0.1407  
## ch                -0.0124093  0.0859627  -0.144   0.8852  
## dia                0.0001498  0.1165310   0.001   0.9990  
## mmse              -0.0131953  0.0058641  -2.250   0.0247 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9431 on 997 degrees of freedom
## Multiple R-squared:  0.01452,    Adjusted R-squared:  0.007602 
## F-statistic: 2.099 on 7 and 997 DF,  p-value: 0.04119
Scatter plot

Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 8
## coefficients de régression

Modèle 6

Modèle
## 
## Call:
## lm(formula = tau_ ~ cholesterol_total + age + sexe + apoe.reg2 + 
##     ch + dia + mmse + imc, data = data_tc_c)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.4160 -0.6762 -0.2356  0.4233  3.7649 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        0.380934   0.388242   0.981   0.3267  
## cholesterol_total -0.074902   0.062408  -1.200   0.2303  
## age                0.002891   0.003381   0.855   0.3927  
## sexe               0.114359   0.063562   1.799   0.0723 .
## apoe.reg2          0.061217   0.047215   1.297   0.1951  
## ch                -0.003842   0.085948  -0.045   0.9644  
## dia                0.026347   0.117116   0.225   0.8221  
## mmse              -0.013154   0.005856  -2.246   0.0249 *
## imc               -0.014106   0.007146  -1.974   0.0487 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9417 on 996 degrees of freedom
## Multiple R-squared:  0.01836,    Adjusted R-squared:  0.01048 
## F-statistic: 2.329 on 8 and 996 DF,  p-value: 0.01769
Scatter plot

Régression linéaire
## Warning in abline(mod, col = "red"): utilisation des deux premiers des 9
## coefficients de régression

Tableau récapitulatif

Model 1Model 2Model 3Model 4Model 5Model 6
(Intercept)-0.02 -0.09 *-0.09 *-0.09  -0.08  -0.08  
[-0.08, 0.04],(0.56)[-0.18, -0.00],(0.05) [-0.18, -0.00],(0.05) [-0.18, 0.01],(0.07) [-0.17, 0.01],(0.09) [-0.17, 0.01],(0.09) 
cholesterol_total-0.01 -0.03  -0.04  -0.04  -0.03  -0.04  
[-0.07, 0.05],(0.75)[-0.09, 0.03],(0.32) [-0.10, 0.03],(0.24) [-0.10, 0.03],(0.24) [-0.10, 0.03],(0.29) [-0.10, 0.02],(0.23) 
age0.03 0.04  0.03  0.04  0.03  0.03  
[-0.03, 0.09],(0.27)[-0.02, 0.09],(0.24) [-0.02, 0.09],(0.25) [-0.02, 0.10],(0.25) [-0.03, 0.09],(0.29) [-0.03, 0.09],(0.39) 
sexe    0.14 *0.14 *0.14 *0.12  0.11  
    [0.01, 0.26],(0.03) [0.01, 0.26],(0.03) [0.01, 0.26],(0.03) [-0.00, 0.25],(0.05) [-0.01, 0.24],(0.07) 
apoe.reg2         0.06  0.06  0.04  0.04  
         [-0.00, 0.11],(0.07) [-0.00, 0.11],(0.07) [-0.01, 0.10],(0.14) [-0.02, 0.10],(0.20) 
ch              -0.02  -0.01  -0.00  
              [-0.19, 0.15],(0.83) [-0.18, 0.16],(0.89) [-0.17, 0.16],(0.96) 
dia              0.00  0.00  0.03  
              [-0.23, 0.23],(0.99) [-0.23, 0.23],(1.00) [-0.20, 0.26],(0.82) 
mmse                   -0.07 *-0.07 *
                   [-0.13, -0.01],(0.02) [-0.13, -0.01],(0.02) 
imc                        -0.06 *
                        [-0.12, -0.00],(0.05) 
N1005    1005     1005     1005     1005     1005     
R20.00 0.01  0.01  0.01  0.01  0.02  
All continuous predictors are mean-centered and scaled by 1 standard deviation. *** p < 0.001; ** p < 0.01; * p < 0.05.
# Export BDD avec scale variable

"data_export = data.frame(abeta42_continu = data2$abeta42_,
                         abeta_42_modalite = data2$abeta42t2,
                         tau_continu = data2$tau_,
                         tau_modalite = data2$taut2,
                         glucose = data2$glucose_jeun,
                         glucose_std = scale(data2$glucose_jeun),
                         triglycerides = data2$triglycerides,
                         triglycerides_std = scale(data2$triglycerides),
                         cholesterol_total = data2$cholesterol_total,
                         cholesterol_total_std = scale(data2$cholesterol_total),
                         cholesterol_hdl = data2$cholesterol_hdl,
                         cholesterol_hdl.std = scale(data2$cholesterol_hdl),
                         cholesterol_ldl = data2$cholesterol_ldl,
                         cholesterol_ldl.std = scale(data2$cholesterol_ldl),
                         education = data2$niveau_etude_reg,
                         age = data2$age,
                         sexe = data2$sexe_reg,
                         apoe = data2$apoe,
                         traitement_chol = data2$ch,
                         traitement_dia = data2$dia,
                         mmse = data2$mmse,
                         imc = data2$imc
                         )"
## [1] "data_export = data.frame(abeta42_continu = data2$abeta42_,\n                         abeta_42_modalite = data2$abeta42t2,\n                         tau_continu = data2$tau_,\n                         tau_modalite = data2$taut2,\n                         glucose = data2$glucose_jeun,\n                         glucose_std = scale(data2$glucose_jeun),\n                         triglycerides = data2$triglycerides,\n                         triglycerides_std = scale(data2$triglycerides),\n                         cholesterol_total = data2$cholesterol_total,\n                         cholesterol_total_std = scale(data2$cholesterol_total),\n                         cholesterol_hdl = data2$cholesterol_hdl,\n                         cholesterol_hdl.std = scale(data2$cholesterol_hdl),\n                         cholesterol_ldl = data2$cholesterol_ldl,\n                         cholesterol_ldl.std = scale(data2$cholesterol_ldl),\n                         education = data2$niveau_etude_reg,\n                         age = data2$age,\n                         sexe = data2$sexe_reg,\n                         apoe = data2$apoe,\n                         traitement_chol = data2$ch,\n                         traitement_dia = data2$dia,\n                         mmse = data2$mmse,\n                         imc = data2$imc\n                         )"
#write.csv(data_export,"//172.27.137.244/g_boilay/alternance/export/data_export.csv")